结构化 JSON 提取
从 Claude 的回复中提取结构化 JSON 数据
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使用 Claude 和工具调用提取结构化 JSON
在本教程中，我们将探索使用 Claude 和工具调用功能从不同类型的输入中提取结构化 JSON 数据的各种示例。我们将定义自定义工具，引导 Claude 为摘要、实体提取、情感分析等任务生成结构良好的 JSON 输出。
如果你想在不使用工具的情况下获取结构化 JSON 数据，请查看我们的”如何启用 JSON 模式“教程。
设置环境
首先，让我们安装所需的库并设置 Claude API 客户端。
%pip install anthropic requests beautifulsoup4import json
import requests
from anthropic import Anthropic
from bs4 import BeautifulSoup
client = Anthropic()
MODEL_NAME = "claude-haiku-4-5"示例 1：文章摘要
在本示例中，我们将使用 Claude 生成文章的 JSON 摘要，包括作者、主题、摘要、连贯性评分、说服力评分和反面观点等字段。
tools = [
{
"name": "print_summary",
"description": "Prints a summary of the article.",
"input_schema": {
"type": "object",
"properties": {
"author": {"type": "string", "description": "Name of the article author"},
"topics": {
"type": "array",
"items": {"type": "string"},
"description": 'Array of topics, e.g. ["tech", "politics"]. Should be as specific as possible, and can overlap.',
},
"summary": {
"type": "string",
"description": "Summary of the article. One or two paragraphs max.",
},
"coherence": {
"type": "integer",
"description": "Coherence of the article's key points, 0-100 (inclusive)",
},
"persuasion": {
"type": "number",
"description": "Article's persuasion score, 0.0-1.0 (inclusive)",
},
},
"required": ["author", "topics", "summary", "coherence", "persuasion", "counterpoint"],
},
}
]
url = "https://www.anthropic.com/news/third-party-testing"
response = requests.get(url, timeout=30)
soup = BeautifulSoup(response.text, "html.parser")
article = " ".join([p.text for p in soup.find_all("p")])
query = f"""
<article>
{article}
</article>
Use the `print_summary` tool.
"""
response = client.messages.create(
model=MODEL_NAME, max_tokens=4096, tools=tools, messages=[{"role": "user", "content": query}]
)
json_summary = None
for content in response.content:
if content.type == "tool_use" and content.name == "print_summary":
json_summary = content.input
break
if json_summary:
print("JSON Summary:")
print(json.dumps(json_summary, indent=2))
else:
print("No JSON summary found in the response.")示例 2：命名实体识别
在本示例中，我们将使用 Claude 对给定文本执行命名实体识别，并以结构化 JSON 格式返回实体。
tools = [
{
"name": "print_entities",
"description": "Prints extract named entities.",
"input_schema": {
"type": "object",
"properties": {
"entities": {
"type": "array",
"items": {
"type": "object",
"properties": {
"name": {"type": "string", "description": "The extracted entity name."},
"type": {
"type": "string",
"description": "The entity type (e.g., PERSON, ORGANIZATION, LOCATION).",
},
"context": {
"type": "string",
"description": "The context in which the entity appears in the text.",
},
},
"required": ["name", "type", "context"],
},
}
},
"required": ["entities"],
},
}
]
text = "John works at Google in New York. He met with Sarah, the CEO of Acme Inc., last week in San Francisco."
query = f"""
<document>
{text}
</document>
Use the print_entities tool.
"""
response = client.messages.create(
model=MODEL_NAME, max_tokens=4096, tools=tools, messages=[{"role": "user", "content": query}]
)
json_entities = None
for content in response.content:
if content.type == "tool_use" and content.name == "print_entities":
json_entities = content.input
break
if json_entities:
print("Extracted Entities (JSON):")
print(json_entities)
else:
print("No entities found in the response.")Extracted Entities (JSON):
{'entities': [{'name': 'John', 'type': 'PERSON', 'context': 'John works at Google in New York.'}, {'name': 'Google', 'type': 'ORGANIZATION', 'context': 'John works at Google in New York.'}, {'name': 'New York', 'type': 'LOCATION', 'context': 'John works at Google in New York.'}, {'name': 'Sarah', 'type': 'PERSON', 'context': 'He met with Sarah, the CEO of Acme Inc., last week in San Francisco.'}, {'name': 'Acme Inc.', 'type': 'ORGANIZATION', 'context': 'He met with Sarah, the CEO of Acme Inc., last week in San Francisco.'}, {'name': 'San Francisco', 'type': 'LOCATION', 'context': 'He met with Sarah, the CEO of Acme Inc., last week in San Francisco.'}]}示例 3：情感分析
在本示例中，我们将使用 Claude 对给定文本执行情感分析，并以结构化 JSON 格式返回情感评分。
tools = [
{
"name": "print_sentiment_scores",
"description": "Prints the sentiment scores of a given text.",
"input_schema": {
"type": "object",
"properties": {
"positive_score": {
"type": "number",
"description": "The positive sentiment score, ranging from 0.0 to 1.0.",
},
"negative_score": {
"type": "number",
"description": "The negative sentiment score, ranging from 0.0 to 1.0.",
},
"neutral_score": {
"type": "number",
"description": "The neutral sentiment score, ranging from 0.0 to 1.0.",
},
},
"required": ["positive_score", "negative_score", "neutral_score"],
},
}
]
text = "The product was okay, but the customer service was terrible. I probably won't buy from them again."
query = f"""
<text>
{text}
</text>
Use the print_sentiment_scores tool.
"""
response = client.messages.create(
model=MODEL_NAME, max_tokens=4096, tools=tools, messages=[{"role": "user", "content": query}]
)
json_sentiment = None
for content in response.content:
if content.type == "tool_use" and content.name == "print_sentiment_scores":
json_sentiment = content.input
break
if json_sentiment:
print("Sentiment Analysis (JSON):")
print(json.dumps(json_sentiment, indent=2))
else:
print("No sentiment analysis found in the response.")Sentiment Analysis (JSON):
{
"negative_score": 0.6,
"neutral_score": 0.3,
"positive_score": 0.1
}示例 4：文本分类
在本示例中，我们将使用 Claude 将给定文本分类到预定义的类别中，并以结构化 JSON 格式返回分类结果。
tools = [
{
"name": "print_classification",
"description": "Prints the classification results.",
"input_schema": {
"type": "object",
"properties": {
"categories": {
"type": "array",
"items": {
"type": "object",
"properties": {
"name": {"type": "string", "description": "The category name."},
"score": {
"type": "number",
"description": "The classification score for the category, ranging from 0.0 to 1.0.",
},
},
"required": ["name", "score"],
},
}
},
"required": ["categories"],
},
}
]
text = "The new quantum computing breakthrough could revolutionize the tech industry."
query = f"""
<document>
{text}
</document>
Use the print_classification tool. The categories can be Politics, Sports, Technology, Entertainment, Business.
"""
response = client.messages.create(
model=MODEL_NAME, max_tokens=4096, tools=tools, messages=[{"role": "user", "content": query}]
)
json_classification = None
for content in response.content:
if content.type == "tool_use" and content.name == "print_classification":
json_classification = content.input
break
if json_classification:
print("Text Classification (JSON):")
print(json.dumps(json_classification, indent=2))
else:
print("No text classification found in the response.")Text Classification (JSON):
{
"categories": [
{
"name": "Politics",
"score": 0.1
},
{
"name": "Sports",
"score": 0.1
},
{
"name": "Technology",
"score": 0.7
},
{
"name": "Entertainment",
"score": 0.1
},
{
"name": "Business",
"score": 0.5
}
]
}示例 5：处理未知键
在某些情况下，你可能事先不知道确切的 JSON 对象结构。在本示例中，我们提供一个开放的 input_schema，并通过提示指导 Claude 如何与工具交互。
tools = [
{
"name": "print_all_characteristics",
"description": "Prints all characteristics which are provided.",
"input_schema": {"type": "object", "additionalProperties": True},
}
]
query = """Given a description of a character, your task is to extract all the characteristics of the character and print them using the print_all_characteristics tool.
The print_all_characteristics tool takes an arbitrary number of inputs where the key is the characteristic name and the value is the characteristic value (age: 28 or eye_color: green).
<description>
The man is tall, with a beard and a scar on his left cheek. He has a deep voice and wears a black leather jacket.
</description>
Now use the print_all_characteristics tool."""
response = client.messages.create(
model=MODEL_NAME,
max_tokens=4096,
tools=tools,
tool_choice={"type": "tool", "name": "print_all_characteristics"},
messages=[{"role": "user", "content": query}],
)
tool_output = None
for content in response.content:
if content.type == "tool_use" and content.name == "print_all_characteristics":
tool_output = content.input
break
if tool_output:
print("Characteristics (JSON):")
print(json.dumps(json_classification, indent=2))
else:
print("Something went wrong.")Characteristics (JSON):
{
"height": "tall",
"facial_hair": "beard",
"facial_features": "scar on left cheek",
"voice": "deep voice",
"clothing": "black leather jacket"
}这些示例演示了如何使用 Claude 和工具调用功能为各种自然语言处理任务提取结构化 JSON 数据。通过定义具有特定输入模式的自定义工具，你可以引导 Claude 生成结构良好的 JSON 输出，这些输出可以在你的应用程序中轻松解析和利用。
Tip
中文读者提示：本章节的代码和输出为英文原文。如需理解具体实现细节，可参考上方中文导读和代码注释。如有疑问，欢迎在 GitHub Issues 讨论。