如何解析 XML 输出

先决条件

本指南假定您熟悉以下概念：

来自不同提供商的 LLM 通常根据其特定的训练数据具有不同的优势。这也意味着有些模型可能在生成除 JSON 之外的其他格式输出时“更好”且更可靠。

本指南将向您展示如何使用 XMLOutputParser 来提示模型生成 XML 输出，然后将该输出解析成可用格式。

note

请记住，大型语言模型是“有漏洞的抽象”！您需要使用具有足够容量来生成格式正确的 XML 的 LLM。

在以下示例中，我们使用了 Anthropic 的 Claude-2 模型（https://docs.anthropic.com/claude/docs），该模型是针对 XML 标签进行优化的模型之一。

%pip install -qU langchain langchain-anthropic

import os
from getpass import getpass

if "ANTHROPIC_API_KEY" not in os.environ:
    os.environ["ANTHROPIC_API_KEY"] = getpass()

让我们从一个简单的模型请求开始。

from langchain_anthropic import ChatAnthropic
from langchain_core.output_parsers import XMLOutputParser
from langchain_core.prompts import PromptTemplate

model = ChatAnthropic(model="claude-2.1", max_tokens_to_sample=512, temperature=0.1)

actor_query = "Generate the shortened filmography for Tom Hanks."

output = model.invoke(
    f"""{actor_query}
Please enclose the movies in <movie></movie> tags"""
)

print(output.content)

API Reference:ChatAnthropic | XMLOutputParser | PromptTemplate

Here is the shortened filmography for Tom Hanks, with movies enclosed in XML tags:

<movie>Splash</movie>
<movie>Big</movie>
<movie>A League of Their Own</movie>
<movie>Sleepless in Seattle</movie>
<movie>Forrest Gump</movie>
<movie>Toy Story</movie>
<movie>Apollo 13</movie>
<movie>Saving Private Ryan</movie>
<movie>Cast Away</movie>
<movie>The Da Vinci Code</movie>

这实际上效果相当不错！但如果能将这个 XML 解析成一个更易于使用的格式就更好了。我们可以使用 XMLOutputParser 来为提示添加默认格式说明，并将输出的 XML 解析成字典：

parser = XMLOutputParser()

# We will add these instructions to the prompt below
parser.get_format_instructions()

'The output should be formatted as a XML file.\n1. Output should conform to the tags below. \n2. If tags are not given, make them on your own.\n3. Remember to always open and close all the tags.\n\nAs an example, for the tags ["foo", "bar", "baz"]:\n1. String "<foo>\n   <bar>\n      <baz></baz>\n   </bar>\n</foo>" is a well-formatted instance of the schema. \n2. String "<foo>\n   <bar>\n   </foo>" is a badly-formatted instance.\n3. String "<foo>\n   <tag>\n   </tag>\n</foo>" is a badly-formatted instance.\n\nHere are the output tags:\n\`\`\`\nNone\n\`\`\`'

prompt = PromptTemplate(
    template="""{query}\n{format_instructions}""",
    input_variables=["query"],
    partial_variables={"format_instructions": parser.get_format_instructions()},
)

chain = prompt | model | parser

output = chain.invoke({"query": actor_query})
print(output)

{'filmography': [{'movie': [{'title': 'Big'}, {'year': '1988'}]}, {'movie': [{'title': 'Forrest Gump'}, {'year': '1994'}]}, {'movie': [{'title': 'Toy Story'}, {'year': '1995'}]}, {'movie': [{'title': 'Saving Private Ryan'}, {'year': '1998'}]}, {'movie': [{'title': 'Cast Away'}, {'year': '2000'}]}]}

我们还可以在提示中添加一些标签来定制输出，以满足我们的需求。你可以而且应该试验在提示的其他部分添加你自己的格式化提示，以增强或替换默认指令：

parser = XMLOutputParser(tags=["movies", "actor", "film", "name", "genre"])

# We will add these instructions to the prompt below
parser.get_format_instructions()

'The output should be formatted as a XML file.\n1. Output should conform to the tags below. \n2. If tags are not given, make them on your own.\n3. Remember to always open and close all the tags.\n\nAs an example, for the tags ["foo", "bar", "baz"]:\n1. String "<foo>\n   <bar>\n      <baz></baz>\n   </bar>\n</foo>" is a well-formatted instance of the schema. \n2. String "<foo>\n   <bar>\n   </foo>" is a badly-formatted instance.\n3. String "<foo>\n   <tag>\n   </tag>\n</foo>" is a badly-formatted instance.\n\nHere are the output tags:\n\`\`\`\n[\'movies\', \'actor\', \'film\', \'name\', \'genre\']\n\`\`\`'

prompt = PromptTemplate(
    template="""{query}\n{format_instructions}""",
    input_variables=["query"],
    partial_variables={"format_instructions": parser.get_format_instructions()},
)


chain = prompt | model | parser

output = chain.invoke({"query": actor_query})

print(output)

{'movies': [{'actor': [{'name': 'Tom Hanks'}, {'film': [{'name': 'Forrest Gump'}, {'genre': 'Drama'}]}, {'film': [{'name': 'Cast Away'}, {'genre': 'Adventure'}]}, {'film': [{'name': 'Saving Private Ryan'}, {'genre': 'War'}]}]}]}

此输出解析器还支持部分分块流式传输。下面是一个示例：

for s in chain.stream({"query": actor_query}):
    print(s)

{'movies': [{'actor': [{'name': 'Tom Hanks'}]}]}
{'movies': [{'actor': [{'film': [{'name': 'Forrest Gump'}]}]}]}
{'movies': [{'actor': [{'film': [{'genre': 'Drama'}]}]}]}
{'movies': [{'actor': [{'film': [{'name': 'Cast Away'}]}]}]}
{'movies': [{'actor': [{'film': [{'genre': 'Adventure'}]}]}]}
{'movies': [{'actor': [{'film': [{'name': 'Saving Private Ryan'}]}]}]}
{'movies': [{'actor': [{'film': [{'genre': 'War'}]}]}]}

后续操作

你已经学会了如何提示模型返回 XML。接下来，请查看关于获取结构化输出更广泛的指南：structured output，了解其他相关技术。

后续操作​

后续操作