如何解析 XML 输出

先决条件

本指南假定读者熟悉以下概念：

不同提供商提供的大型语言模型，其优势往往取决于它们所训练的具体数据。这也意味着，某些模型在生成非JSON格式的输出时，可能表现得“更好”且更可靠。

本指南将向您展示如何使用 XMLOutputParser 提示模型生成 XML 输出，然后将其解析为可用格式。

笔记

请记住，大型语言模型是存在漏洞的抽象！你需要使用具备足够容量的LLM来生成格式正确的XML。

在以下示例中，我们使用 Anthropic 的 Claude-2 模型 (https://docs.anthropic.com/claude/docs)，该模型是专为 XML 标签优化的模型之一。

%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()

让我们先向模型提出一个简单的请求。

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 参考：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>

这实际上效果不错！但将其解析为更易用的格式会更好。我们可以使用 XMLOutputParser 既向提示中添加默认格式说明，又将输出的 XML 解析为字典：

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'}]}]}

我们还可以添加一些标签，以根据我们的需求定制输出。你可以并且应该在提示的其他部分尝试添加自己的格式化提示，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数据块的流式处理。以下是示例：

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'}]}]}]}

下一步

您现在已经学会了如何提示模型返回XML。接下来，请查看获取结构化输出的完整指南，了解其他相关技术。

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