使用引用
让 Claude 在回复中标注信息来源
🌐 查看英文原文 | 源码 Notebook
引用（Citations）
Claude API 的引用功能使 Claude 能够在回答关于文档的问题时提供详细的引用。引用是许多 LLM 驱动应用中的重要功能，可帮助用户追踪和验证回复中信息的来源。
引用功能支持以下模型： * claude-sonnet-4-6 * claude-3-5-haiku-20241022
引用功能是基于 prompt 的引用技术的替代方案。使用该功能有以下优势： - 基于 prompt 的技术通常需要 Claude 输出它想引用的源文档中的完整引文，这会增加输出 token，从而增加成本。 - 引用功能不会返回指向未作为有效来源提供的文档或位置的引用。 - 在测试中，我们发现引用功能生成的引用比基于 prompt 的技术具有更高的召回率和精确率。
引用功能的文档可以在这里找到。
环境配置
首先，让我们安装所需的库并初始化 Anthropic 客户端。
!pip install anthropic --quietimport json
import os
import anthropic
ANTHROPIC_API_KEY = os.environ.get("ANTHROPIC_API_KEY")
# ANTHROPIC_API_KEY = "" # Put your API key here!
client = anthropic.Anthropic(api_key=ANTHROPIC_API_KEY)文档类型
引用功能支持三种不同的文档类型。输出的引用类型取决于被引用文档的类型：
- 纯文本文档引用 → 字符位置格式
- PDF 文档引用 → 页面位置格式
- 自定义内容文档引用 → 内容块位置格式
我们将在下面的示例中探索如何使用每种类型。
纯文本文档
使用纯文本文档引用时，你将文档作为原始文本提供给模型。你可以提供一个或多个文档。这些文本会自动按句子进行分块。模型会根据需要引用这些句子。模型可以在单个引用中同时引用多个句子，但不会引用小于一个句子的文本。
除了输出的文本外，API 响应还将包含所有引用的结构化数据。
让我们看一个完整的示例，使用一个虚构公司 PetWorld 的帮助中心客户聊天机器人。
# Read all help center articles and create a list of documents
articles_dir = "./data/help_center_articles"
documents = []
for filename in sorted(os.listdir(articles_dir)):
if filename.endswith(".txt"):
with open(os.path.join(articles_dir, filename)) as f:
content = f.read()
# Split into title and body
title_line, body = content.split("\n", 1)
title = title_line.replace("title: ", "")
documents.append(
{
"type": "document",
"source": {"type": "text", "media_type": "text/plain", "data": body},
"title": title,
"citations": {"enabled": True},
}
)
QUESTION = "I just checked out, where is my order tracking number? Track package is not available on the website yet for my order."
# Add the question to the content
content = documents
response = client.messages.create(
model="claude-sonnet-4-6",
temperature=0.0,
max_tokens=1024,
system="You are a customer support bot working for PetWorld. Your task is to provide short, helpful answers to user questions. Since you are in a chat interface avoid providing extra details. You will be given access to PetWorld's help center articles to help you answer questions.",
messages=[
{"role": "user", "content": documents},
{
"role": "user",
"content": [{"type": "text", "text": f"Here is the user's question: {QUESTION}"}],
},
],
)
def visualize_raw_response(response):
raw_response = {"content": []}
print("\n" + "=" * 80 + "\nRaw response:\n" + "=" * 80)
for content in response.content:
if content.type == "text":
block = {"type": "text", "text": content.text}
if hasattr(content, "citations") and content.citations:
block["citations"] = []
for citation in content.citations:
citation_dict = {
"type": citation.type,
"cited_text": citation.cited_text,
"document_title": citation.document_title,
}
if citation.type == "page_location":
citation_dict.update(
{
"start_page_number": citation.start_page_number,
"end_page_number": citation.end_page_number,
}
)
block["citations"].append(citation_dict)
raw_response["content"].append(block)
return json.dumps(raw_response, indent=2)
print(visualize_raw_response(response))
================================================================================
Raw response:
================================================================================
{
"content": [
{
"type": "text",
"text": "Based on the documentation, I can explain why you don't see tracking yet: "
},
{
"type": "text",
"text": "You'll receive an email with your tracking number once your order ships. If you don't receive a tracking number within 48 hours of your order confirmation, please contact our customer support team for assistance.",
"citations": [
{
"type": "char_location",
"cited_text": "Once your order ships, you'll receive an email with a tracking number. ",
"document_title": "Order Tracking Information"
},
{
"type": "char_location",
"cited_text": "If you haven't received a tracking number within 48 hours of your order confirmation, please contact our customer support team.",
"document_title": "Order Tracking Information"
}
]
},
{
"type": "text",
"text": "\n\nSince you just checked out, your order likely hasn't shipped yet. Once it ships, you'll receive the tracking information via email."
}
]
}可视化引用
利用引用数据，我们可以创建这样的界面：
- 向用户准确展示信息的来源
- 直接链接到源文档
- 在上下文中高亮显示被引用的文本
- 通过透明的来源标注建立信任
下面是一个简单的可视化函数，它将 Claude 的结构化引用转换为带有编号引用标记的可读格式，类似于学术论文。
该函数接收 Claude 的响应对象并输出： - 带有编号引用标记的文本（例如，“答案 [1] 包含了这个事实 [2]”） - 一个编号的引用列表，显示每条被引用的文本及其来源文档
def visualize_citations(response):
"""
Takes a response object and returns a string with numbered citations.
Example output: "here is the plain text answer [1][2] here is some more text [3]"
with a list of citations below.
"""
# Dictionary to store unique citations
citations_dict = {}
citation_counter = 1
# Final formatted text
formatted_text = ""
citations_list = []
print("\n" + "=" * 80 + "\nFormatted response:\n" + "=" * 80)
for content in response.content:
if content.type == "text":
text = content.text
if hasattr(content, "citations") and content.citations:
# Sort citations by their appearance in the text
def get_sort_key(citation):
if hasattr(citation, "start_char_index"):
return citation.start_char_index
elif hasattr(citation, "start_page_number"):
return citation.start_page_number
elif hasattr(citation, "start_block_index"):
return citation.start_block_index
return 0 # fallback
sorted_citations = sorted(content.citations, key=get_sort_key)
# Process each citation
for citation in sorted_citations:
doc_title = citation.document_title
cited_text = citation.cited_text.replace("\n", " ").replace("\r", " ")
# Remove any multiple spaces that might have been created
cited_text = " ".join(cited_text.split())
# Create a unique key for this citation
citation_key = f"{doc_title}:{cited_text}"
# If this is a new citation, add it to our dictionary
if citation_key not in citations_dict:
citations_dict[citation_key] = citation_counter
citations_list.append(
f'[{citation_counter}] "{cited_text}" found in "{doc_title}"'
)
citation_counter += 1
# Add the citation number to the text
citation_num = citations_dict[citation_key]
text += f" [{citation_num}]"
formatted_text += text
# Combine the formatted text with the citations list
final_output = formatted_text + "\n\n" + "\n".join(citations_list)
return final_output
formatted_response = visualize_citations(response)
print(formatted_response)
================================================================================
Formatted response:
================================================================================
Based on the documentation, I can explain why you don't see tracking yet: You'll receive an email with your tracking number once your order ships. If you don't receive a tracking number within 48 hours of your order confirmation, please contact our customer support team for assistance. [1] [2]
Since you just checked out, your order likely hasn't shipped yet. Once it ships, you'll receive the tracking information via email.
[1] "Once your order ships, you'll receive an email with a tracking number." found in "Order Tracking Information"
[2] "If you haven't received a tracking number within 48 hours of your order confirmation, please contact our customer support team." found in "Order Tracking Information"PDF 文档
处理 PDF 时，Claude 可以提供引用特定页码的引用，便于追踪信息来源。以下是 PDF 引用的工作方式：
- PDF 文档内容以 base64 编码数据的形式提供
- 文本会自动按句子进行分块
- 引用包含找到信息的页码（从 1 开始索引）
- 模型可以在单个引用中同时引用多个句子，但不会引用小于一个句子的文本
- 虽然会处理图像，但目前只能引用文本内容
下面是使用 Constitutional AI 论文来演示 PDF 引用的示例：
import base64
import json
# Read and encode the PDF
pdf_path = "data/Constitutional AI.pdf"
with open(pdf_path, "rb") as f:
pdf_data = base64.b64encode(f.read()).decode()
pdf_response = client.messages.create(
model="claude-sonnet-4-6",
temperature=0.0,
max_tokens=1024,
messages=[
{
"role": "user",
"content": [
{
"type": "document",
"source": {"type": "base64", "media_type": "application/pdf", "data": pdf_data},
"title": "Constitutional AI Paper",
"citations": {"enabled": True},
},
{"type": "text", "text": "What is the main idea of Constitutional AI?"},
],
}
],
)
print(visualize_raw_response(pdf_response))
print(visualize_citations(pdf_response))
================================================================================
Raw response:
================================================================================
{
"content": [
{
"type": "text",
"text": "Based on the paper, here are the key aspects of Constitutional AI:\n\n"
},
{
"type": "text",
"text": "Constitutional AI is a method for training a harmless AI assistant through self-improvement, without any human labels identifying harmful outputs. The only human oversight is provided through a list of rules or principles, hence the name \"Constitutional AI\".",
"citations": [
{
"type": "page_location",
"cited_text": "We experiment with methods for training a harmless AI assistant through self\u0002improvement, without any human labels identifying harmful outputs. The only human\r\noversight is provided through a list of rules or principles, and so we refer to the method as\r\n\u2018Constitutional AI\u2019. ",
"document_title": "Constitutional AI Paper",
"start_page_number": 1,
"end_page_number": 2
}
]
},
{
"type": "text",
"text": "\n\nThe process involves two main phases:\n\n1. Supervised Learning Phase:\n"
},
{
"type": "text",
"text": "In this phase, they sample from an initial model, generate self-critiques and revisions, and then finetune the original model on revised responses.",
"citations": [
{
"type": "page_location",
"cited_text": "In the supervised phase we sample from an initial model, then generate\r\nself-critiques and revisions, and then finetune the original model on revised responses. ",
"document_title": "Constitutional AI Paper",
"start_page_number": 1,
"end_page_number": 2
}
]
},
{
"type": "text",
"text": "\n\n2. Reinforcement Learning Phase:\n"
},
{
"type": "text",
"text": "In this phase, they:\n- Sample from the finetuned model\n- Use a model to evaluate which of two samples is better\n- Train a preference model from this dataset of AI preferences\n- Use \"RL from AI Feedback\" (RLAIF)",
"citations": [
{
"type": "page_location",
"cited_text": "In\r\nthe RL phase, we sample from the finetuned model, use a model to evaluate which of the\r\ntwo samples is better, and then train a preference model from this dataset of AI prefer\u0002ences. We then train with RL using the preference model as the reward signal, i.e. we\r\nuse \u2018RL from AI Feedback\u2019 (RLAIF). ",
"document_title": "Constitutional AI Paper",
"start_page_number": 1,
"end_page_number": 2
}
]
},
{
"type": "text",
"text": "\n\nThe key outcomes are:\n\n"
},
{
"type": "text",
"text": "- They are able to train a harmless but non-evasive AI assistant that engages with harmful queries by explaining its objections to them\n- Both the SL and RL methods can leverage chain-of-thought style reasoning to improve human-judged performance and transparency of AI decision making\n- These methods make it possible to control AI behavior more precisely and with far fewer human labels",
"citations": [
{
"type": "page_location",
"cited_text": "As a result we are able to train a harmless but non\u0002evasive AI assistant that engages with harmful queries by explaining its objections to them.\r\nBoth the SL and RL methods can leverage chain-of-thought style reasoning to improve the\r\nhuman-judged performance and transparency of AI decision making. These methods make\r\nit possible to control AI behavior more precisely and with far fewer human labels.\r\n",
"document_title": "Constitutional AI Paper",
"start_page_number": 1,
"end_page_number": 2
}
]
},
{
"type": "text",
"text": "\n\n"
},
{
"type": "text",
"text": "The ultimate goal is not to completely remove human supervision, but rather to make it more efficient, transparent and targeted. While this work reduces reliance on human supervision for harmlessness, they still relied on human supervision in the form of helpfulness labels. The researchers expect it is possible to achieve helpfulness and instruction-following without human feedback, starting from only a pretrained LM and extensive prompting, but leave this for future work.",
"citations": [
{
"type": "page_location",
"cited_text": "By removing human feedback labels for harmlessness, we have moved further away from reliance on human\r\nsupervision, and closer to the possibility of a self-supervised approach to alignment. However, in this work\r\nwe still relied on human supervision in the form of helpfulness labels. We expect it is possible to achieve help\u0002fulness and instruction-following without human feedback, starting from only a pretrained LM and extensive\r\nprompting, but we leave this for future work.\r\nOur ultimate goal is not to remove human supervision entirely, but to make it more efficient, transparent, and\r\ntargeted. ",
"document_title": "Constitutional AI Paper",
"start_page_number": 15,
"end_page_number": 16
}
]
}
]
}
================================================================================
Formatted response:
================================================================================
Based on the paper, here are the key aspects of Constitutional AI:
Constitutional AI is a method for training a harmless AI assistant through self-improvement, without any human labels identifying harmful outputs. The only human oversight is provided through a list of rules or principles, hence the name "Constitutional AI". [1]
The process involves two main phases:
1. Supervised Learning Phase:
In this phase, they sample from an initial model, generate self-critiques and revisions, and then finetune the original model on revised responses. [2]
2. Reinforcement Learning Phase:
In this phase, they:
- Sample from the finetuned model
- Use a model to evaluate which of two samples is better
- Train a preference model from this dataset of AI preferences
- Use "RL from AI Feedback" (RLAIF) [3]
The key outcomes are:
- They are able to train a harmless but non-evasive AI assistant that engages with harmful queries by explaining its objections to them
- Both the SL and RL methods can leverage chain-of-thought style reasoning to improve human-judged performance and transparency of AI decision making
- These methods make it possible to control AI behavior more precisely and with far fewer human labels [4]
The ultimate goal is not to completely remove human supervision, but rather to make it more efficient, transparent and targeted. While this work reduces reliance on human supervision for harmlessness, they still relied on human supervision in the form of helpfulness labels. The researchers expect it is possible to achieve helpfulness and instruction-following without human feedback, starting from only a pretrained LM and extensive prompting, but leave this for future work. [5]
[1] "We experiment with methods for training a harmless AI assistant through selfimprovement, without any human labels identifying harmful outputs. The only human oversight is provided through a list of rules or principles, and so we refer to the method as ‘Constitutional AI’." found in "Constitutional AI Paper"
[2] "In the supervised phase we sample from an initial model, then generate self-critiques and revisions, and then finetune the original model on revised responses." found in "Constitutional AI Paper"
[3] "In the RL phase, we sample from the finetuned model, use a model to evaluate which of the two samples is better, and then train a preference model from this dataset of AI preferences. We then train with RL using the preference model as the reward signal, i.e. we use ‘RL from AI Feedback’ (RLAIF)." found in "Constitutional AI Paper"
[4] "As a result we are able to train a harmless but nonevasive AI assistant that engages with harmful queries by explaining its objections to them. Both the SL and RL methods can leverage chain-of-thought style reasoning to improve the human-judged performance and transparency of AI decision making. These methods make it possible to control AI behavior more precisely and with far fewer human labels." found in "Constitutional AI Paper"
[5] "By removing human feedback labels for harmlessness, we have moved further away from reliance on human supervision, and closer to the possibility of a self-supervised approach to alignment. However, in this work we still relied on human supervision in the form of helpfulness labels. We expect it is possible to achieve helpfulness and instruction-following without human feedback, starting from only a pretrained LM and extensive prompting, but we leave this for future work. Our ultimate goal is not to remove human supervision entirely, but to make it more efficient, transparent, and targeted." found in "Constitutional AI Paper"自定义内容文档
纯文本文档会自动按句子分块，而自定义内容文档则让你完全控制引用的粒度。这种 API 形式允许你：
- 定义任意大小的自定义分块
- 控制最小引用单位
- 针对不适合按句子分块的文档进行优化
在下面的示例中，我们使用与上面纯文本示例相同的帮助中心文章，但不允许按句子引用，而是将每篇文章视为一个单独的分块。这展示了文档类型的选择如何影响引用行为和粒度。你会注意到，cited_text 是整篇文章，而不是源文章中的单个句子。
# Read all help center articles and create a list of custom content documents
articles_dir = "./data/help_center_articles"
documents = []
for filename in sorted(os.listdir(articles_dir)):
if filename.endswith(".txt"):
with open(os.path.join(articles_dir, filename)) as f:
content = f.read()
# Split into title and body
title_line, body = content.split("\n", 1)
title = title_line.replace("title: ", "")
documents.append(
{
"type": "document",
"source": {"type": "content", "content": [{"type": "text", "text": body}]},
"title": title,
"citations": {"enabled": True},
}
)
QUESTION = "I just checked out, where is my order tracking number? Track package is not available on the website yet for my order."
custom_content_response = client.messages.create(
model="claude-sonnet-4-6",
temperature=0.0,
max_tokens=1024,
system="You are a customer support bot working for PetWorld. Your task is to provide short, helpful answers to user questions. Since you are in a chat interface avoid providing extra details. You will be given access to PetWorld's help center articles to help you answer questions.",
messages=[
{"role": "user", "content": documents},
{
"role": "user",
"content": [{"type": "text", "text": f"Here is the user's question: {QUESTION}"}],
},
],
)
print(visualize_raw_response(custom_content_response))
print(visualize_citations(custom_content_response))
================================================================================
Raw response:
================================================================================
{
"content": [
{
"type": "text",
"text": "You should receive an email with your tracking number once your order ships. If it's been less than 48 hours since your order confirmation, please wait as the tracking number may not be available yet. If you haven't received a tracking number after 48 hours, please contact our customer support team for assistance.",
"citations": [
{
"type": "content_block_location",
"cited_text": "Once your order ships, you'll receive an email with a tracking number. To track your package, log in to your PetWorld account and go to \"Order History.\" Click on the order you want to track and select \"Track Package.\" This will show you the current status and estimated delivery date. You can also enter the tracking number directly on our shipping partner's website for more detailed information. If you haven't received a tracking number within 48 hours of your order confirmation, please contact our customer support team.",
"document_title": "Order Tracking Information"
}
]
}
]
}
================================================================================
Formatted response:
================================================================================
You should receive an email with your tracking number once your order ships. If it's been less than 48 hours since your order confirmation, please wait as the tracking number may not be available yet. If you haven't received a tracking number after 48 hours, please contact our customer support team for assistance. [1]
[1] "Once your order ships, you'll receive an email with a tracking number. To track your package, log in to your PetWorld account and go to "Order History." Click on the order you want to track and select "Track Package." This will show you the current status and estimated delivery date. You can also enter the tracking number directly on our shipping partner's website for more detailed information. If you haven't received a tracking number within 48 hours of your order confirmation, please contact our customer support team." found in "Order Tracking Information"使用 Context 字段
context 字段允许你提供关于文档的额外信息，Claude 可以在生成回复时使用这些信息，但这些信息不会被引用。这对于以下场景很有用：
- 提供关于文档的元数据（例如，发布日期、作者）
- 上下文检索
- 包含不应被直接引用的使用说明或上下文
在下面的示例中，我们提供了一篇带有 context 字段中警告的会员计划文章。请注意 Claude 如何使用 context 中的信息来辅助其回复，但 context 字段的内容不能用于引用。
import json
# Create a document with context field
document = {
"type": "document",
"source": {
"type": "text",
"media_type": "text/plain",
"data": "PetWorld offers a loyalty program where customers earn 1 point for every dollar spent. Once you accumulate 100 points, you'll receive a $5 reward that can be used on your next purchase. Points expire 12 months after they are earned. You can check your point balance in your account dashboard or by asking customer service.",
},
"title": "Loyalty Program Details",
"context": "WARNING: This article has not been updated in 12 months. Content may be out of date. Be sure to inform the user this content may be incorrect after providing guidance.",
"citations": {"enabled": True},
}
QUESTION = "How does PetWorld's loyalty program work? When do points expire?"
context_response = client.messages.create(
model="claude-sonnet-4-6",
temperature=0.0,
max_tokens=1024,
messages=[{"role": "user", "content": [document, {"type": "text", "text": QUESTION}]}],
)
print(visualize_raw_response(context_response))
print(visualize_citations(context_response))
================================================================================
Raw response:
================================================================================
{
"content": [
{
"type": "text",
"text": "Let me explain PetWorld's loyalty program based on the provided information:\n\n"
},
{
"type": "text",
"text": "PetWorld's loyalty program is straightforward - you earn 1 point for every dollar you spend. These points can be redeemed once you reach 100 points, which will get you a $5 reward that you can use on your next purchase.",
"citations": [
{
"type": "char_location",
"cited_text": "PetWorld offers a loyalty program where customers earn 1 point for every dollar spent. Once you accumulate 100 points, you'll receive a $5 reward that can be used on your next purchase. ",
"document_title": "Loyalty Program Details"
}
]
},
{
"type": "text",
"text": "\n\n"
},
{
"type": "text",
"text": "Points have an expiration period of 12 months from the date they are earned.",
"citations": [
{
"type": "char_location",
"cited_text": "Points expire 12 months after they are earned. ",
"document_title": "Loyalty Program Details"
}
]
},
{
"type": "text",
"text": "\n\n"
},
{
"type": "text",
"text": "You can easily keep track of your points by either checking your account dashboard or contacting customer service.",
"citations": [
{
"type": "char_location",
"cited_text": "You can check your point balance in your account dashboard or by asking customer service.",
"document_title": "Loyalty Program Details"
}
]
},
{
"type": "text",
"text": "\n\nPlease note that since this information is from an article that hasn't been updated in 12 months, some details of the program may have changed. It would be best to verify the current terms with PetWorld directly."
}
]
}
================================================================================
Formatted response:
================================================================================
Let me explain PetWorld's loyalty program based on the provided information:
PetWorld's loyalty program is straightforward - you earn 1 point for every dollar you spend. These points can be redeemed once you reach 100 points, which will get you a $5 reward that you can use on your next purchase. [1]
Points have an expiration period of 12 months from the date they are earned. [2]
You can easily keep track of your points by either checking your account dashboard or contacting customer service. [3]
Please note that since this information is from an article that hasn't been updated in 12 months, some details of the program may have changed. It would be best to verify the current terms with PetWorld directly.
[1] "PetWorld offers a loyalty program where customers earn 1 point for every dollar spent. Once you accumulate 100 points, you'll receive a $5 reward that can be used on your next purchase." found in "Loyalty Program Details"
[2] "Points expire 12 months after they are earned." found in "Loyalty Program Details"
[3] "You can check your point balance in your account dashboard or by asking customer service." found in "Loyalty Program Details"PDF 高亮显示
PDF 引用的一个局限性是只返回页码。你可以使用第三方库将返回的被引用文本与页面内容匹配，以引起对被引用内容的注意。此单元格演示了使用 Claude 和 PyMuPDF 进行 PDF 引用高亮显示，创建了一个新的带注释 PDF：
import fitz # PyMuPDF
# Setup paths and read PDF
pdf_path = "data/Amazon-com-Inc-2023-Shareholder-Letter.pdf"
output_pdf_path = "data/Amazon-com-Inc-2023-Shareholder-Letter-highlighted.pdf"
# Read and encode the PDF
with open(pdf_path, "rb") as f:
pdf_data = base64.b64encode(f.read()).decode()
response = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
temperature=0,
messages=[
{
"role": "user",
"content": [
{
"type": "document",
"source": {"type": "base64", "media_type": "application/pdf", "data": pdf_data},
"title": "Amazon 2023 Shareholder Letter",
"citations": {"enabled": True},
},
{
"type": "text",
"text": "What was Amazon's total revenue in 2023 and how much did it grow year-over-year?",
},
],
}
],
)
print(visualize_raw_response(response))
# Collect PDF citations
pdf_citations = []
for content in response.content:
if hasattr(content, "citations") and content.citations:
for citation in content.citations:
if citation.type == "page_location":
pdf_citations.append(citation)
doc = fitz.open(pdf_path)
# Process each citation
for citation in pdf_citations:
if citation.type == "page_location":
text_to_find = citation.cited_text.replace("\u0002", "")
start_page = citation.start_page_number - 1 # Convert to 0-based index
end_page = citation.end_page_number - 2
# Process each page in the citation range
for page_num in range(start_page, end_page + 1):
page = doc[page_num]
text_instances = page.search_for(text_to_find.strip())
if text_instances:
print(f"Found cited text on page {page_num + 1}")
for inst in text_instances:
highlight = page.add_highlight_annot(inst)
highlight.set_colors({"stroke": (1, 1, 0)}) # Yellow highlight
highlight.update()
else:
print(f"{text_to_find} not found on page {page_num + 1}")
# Save the new PDF
doc.save(output_pdf_path)
doc.close()
print(f"\nCreated highlighted PDF at: {output_pdf_path}")
================================================================================
Raw response:
================================================================================
{
"content": [
{
"type": "text",
"text": "According to the letter, "
},
{
"type": "text",
"text": "Amazon's total revenue grew 12% year-over-year (\"YoY\") from $514B to $575B in 2023",
"citations": [
{
"type": "page_location",
"cited_text": "In 2023, Amazon\u2019s total revenue grew 12% year-over-year (\u201cYoY\u201d) from $514B to $575B. ",
"document_title": "Amazon 2023 Shareholder Letter",
"start_page_number": 1,
"end_page_number": 2
}
]
},
{
"type": "text",
"text": ".\n\nBreaking this down by segment:\n"
},
{
"type": "text",
"text": "\n- North America revenue increased 12% YoY from $316B to $353B\n- International revenue grew 11% YoY from $118B to $131B \n- AWS revenue increased 13% YoY from $80B to $91B",
"citations": [
{
"type": "page_location",
"cited_text": "By segment, North\r\nAmerica revenue increased 12% YoY from $316B to $353B, International revenue grew 11% YoY from\r\n$118B to $131B, and AWS revenue increased 13% YoY from $80B to $91B.\r\n",
"document_title": "Amazon 2023 Shareholder Letter",
"start_page_number": 1,
"end_page_number": 2
}
]
}
]
}
Found cited text on page 1
Found cited text on page 1
Created highlighted PDF at: data/Amazon-com-Inc-2023-Shareholder-Letter-highlighted.pdf
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。