PDF 上传与摘要

解析 PDF 并传给 Claude 进行摘要

🌐 查看英文原文 | 源码 Notebook

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通过 API 将 PDF “上传”给 Claude

Claude.ai 有一个非常棒的功能——可以上传 PDF。让我们在 notebook 中模拟这个功能，然后通过总结一篇长篇 PDF 来测试它。

我们首先安装 Anthropic 客户端，并创建一个实例，在本教程的后续部分中使用。

%pip install anthropic

from anthropic import Anthropic
# While PDF support is in beta, you must pass in the correct beta header
client = Anthropic(default_headers={
    "anthropic-beta": "pdfs-2024-09-25"
  }
)
# For now, only claude-sonnet-4-6 supports PDFs
MODEL_NAME = "claude-sonnet-4-6"

我们已经有一个 PDF 文件在 ../multimodal/documents 目录中。我们将把 PDF 文件转换为 base64 编码的字节。这是 Claude API 中 PDF 文档块 所需的格式。请注意，这种提取方式适用于文本和视觉元素（如图表和图形）。

import base64


# Start by reading in the PDF and encoding it as base64
file_name = "../multimodal/documents/constitutional-ai-paper.pdf"
with open(file_name, "rb") as pdf_file:
  binary_data = pdf_file.read()
  base64_encoded_data = base64.standard_b64encode(binary_data)
  base64_string = base64_encoded_data.decode("utf-8")

论文下载并载入内存后，我们可以让 Claude 对它执行各种有趣的任务。我们将把文档和一个简单的问题一起传递给模型。

prompt = """
Please do the following:
1. Summarize the abstract at a kindergarten reading level. (In <kindergarten_abstract> tags.)
2. Write the Methods section as a recipe from the Moosewood Cookbook. (In <moosewood_methods> tags.)
3. Compose a short poem epistolizing the results in the style of Homer. (In <homer_results> tags.)
"""
messages = [
    {
        "role": 'user',
        "content": [
            {"type": "document", "source": {"type": "base64", "media_type": "application/pdf", "data": base64_string}},
            {"type": "text", "text": prompt}
        ]
    }
]

def get_completion(client, messages):
    return client.messages.create(
        model=MODEL_NAME,
        max_tokens=2048,
        messages=messages
    ).content[0].text

completion = get_completion(client, messages)
print(completion)

<kindergarten_abstract>
The scientists wanted to make computer helpers that are nice and don't do bad things. They taught the computer how to check its own work and fix its mistakes without humans having to tell it what's wrong every time. It's like teaching the computer to be its own teacher! They gave the computer some basic rules to follow, like "be kind" and "don't hurt others." Now the computer can answer questions in a helpful way while still being nice and explaining why some things aren't okay to do.
</kindergarten_abstract>

<moosewood_methods>
Constitutional AI Training Stew
A nourishing recipe for teaching computers to be helpful and harmless

Ingredients:
- 1 helpful AI model, pre-trained
- A bundle of constitutional principles
- Several cups of training data
- A dash of human feedback (for helpfulness only)
- Chain-of-thought reasoning, to taste

Method:
1. Begin by gently simmering your pre-trained AI model in a bath of helpful training data until it responds reliably to instructions.

2. In a separate bowl, combine your constitutional principles with some example conversations. Mix well until principles are evenly distributed.

3. Take your helpful AI and ask it to generate responses to challenging prompts. Have it critique its own responses using the constitutional principles, then revise accordingly. Repeat this process 3-4 times until responses are properly seasoned with harmlessness.

4. For the final garnish, add chain-of-thought reasoning and allow the model to explain its decisions step by step.

5. Let rest while training a preference model using AI feedback rather than human labels.

Serves: All users seeking helpful and harmless AI assistance
Cook time: Multiple training epochs
Note: Best results come from consistent application of principles throughout the process
</moosewood_methods>

<homer_results>
O Muse! Sing of the AI that learned to be
Both helpful and harmless, guided by philosophy
Without human labels marking right from wrong
The model learned wisdom, grew capable and strong

Through cycles of critique and thoughtful revision
It mastered the art of ethical decision
Better than models trained by human hand
More transparent in purpose, more clear in command

No longer evasive when faced with hard themes
But engaging with wisdom that thoughtfully deems
What counsel to give, what bounds to maintain
Teaching mortals while keeping its principles plain

Thus did the researchers discover a way
To scale up alignment for use every day
Through constitutional rules and self-guided learning
The path to safe AI they found themselves earning
</homer_results>

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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。