ZeroxPDFLoader

ZeroxPDFLoader 是一个文档加载器，它利用 Zerox 库。Zerox 将 PDF 文档转换为图像，使用具备视觉能力的语言模型对其进行处理，并生成结构化的 Markdown 表示。该加载器支持异步操作，并提供页面级别的文档提取。

集成详情

类别	包	本地	可序列化	JS 支持
ZeroxPDFLoader	langchain_community	❌	❌	❌

加载器功能

来源	文档延迟加载	原生异步支持
ZeroxPDFLoader	✅	❌

设置

凭证

需要在环境变量中设置适当的凭据。该加载器支持多种不同的模型和模型提供商。请参阅下面的“用法”标题以查看一些示例，或查阅 Zerox 文档以获取支持模型的完整列表。

安装

要使用 ZeroxPDFLoader，您需要安装 zerox 包。同时确保已安装 langchain-community。

pip install zerox langchain-community

初始化

ZeroxPDFLoader 通过将每个 PDF 页面转换为图像并异步处理，从而实现了使用具备视觉能力的语言模型进行文本提取。要使用此加载器，您需要指定一个模型并为 Zerox 配置任何必要的环境变量，例如 API 密钥。

如果您在 Jupyter Notebook 等环境中工作，您可能需要通过使用 nest_asyncio 来处理异步代码。您可以按如下方式设置：

import nest_asyncio
nest_asyncio.apply()

import os

# use nest_asyncio (only necessary inside of jupyter notebook)
import nest_asyncio
from langchain_community.document_loaders.pdf import ZeroxPDFLoader

nest_asyncio.apply()

# Specify the url or file path for the PDF you want to process
# In this case let's use pdf from web
file_path = "https://assets.ctfassets.net/f1df9zr7wr1a/soP1fjvG1Wu66HJhu3FBS/034d6ca48edb119ae77dec5ce01a8612/OpenAI_Sacra_Teardown.pdf"

# Set up necessary env vars for a vision model
os.environ["OPENAI_API_KEY"] = (
    "zK3BAhQUmbwZNoHoOcscBwQdwi3oc3hzwJmbgdZ"  ## your-api-key
)

# Initialize ZeroxPDFLoader with the desired model
loader = ZeroxPDFLoader(file_path=file_path, model="azure/gpt-4o-mini")

API 参考:ZeroxPDFLoader

加载

# Load the document and look at the first page:
documents = loader.load()
documents[0]

Document(metadata={'source': 'https://assets.ctfassets.net/f1df9zr7wr1a/soP1fjvG1Wu66HJhu3FBS/034d6ca48edb119ae77dec5ce01a8612/OpenAI_Sacra_Teardown.pdf', 'page': 1, 'num_pages': 5}, page_content='# OpenAI\n\nOpenAI is an AI research laboratory.\n\n#ai-models #ai\n\n## Revenue\n- **$1,000,000,000**  \n  2023\n\n## Valuation\n- **$28,000,000,000**  \n  2023\n\n## Growth Rate (Y/Y)\n- **400%**  \n  2023\n\n## Funding\n- **$11,300,000,000**  \n  2023\n\n---\n\n## Details\n- **Headquarters:** San Francisco, CA\n- **CEO:** Sam Altman\n\n[Visit Website](#)\n\n---\n\n## Revenue\n### ARR ($M)  | Growth\n--- | ---\n$1000M  | 456%\n$750M   | \n$500M   | \n$250M   | $36M\n$0     | $200M\n\nis on track to hit $1B in annual recurring revenue by the end of 2023, up about 400% from an estimated $200M at the end of 2022.\n\nOpenAI overall lost about $540M last year while developing ChatGPT, and those losses are expected to increase dramatically in 2023 with the growth in popularity of their consumer tools, with CEO Sam Altman remarking that OpenAI is likely to be "the most capital-intensive startup in Silicon Valley history."\n\nThe reason for that is operating ChatGPT is massively expensive. One analysis of ChatGPT put the running cost at about $700,000 per day taking into account the underlying costs of GPU hours and hardware. That amount—derived from the 175 billion parameter-large architecture of GPT-3—would be even higher with the 100 trillion parameters of GPT-4.\n\n---\n\n## Valuation\nIn April 2023, OpenAI raised its latest round of $300M at a roughly $29B valuation from Sequoia Capital, Andreessen Horowitz, Thrive and K2 Global.\n\nAssuming OpenAI was at roughly $300M in ARR at the time, that would have given them a 96x forward revenue multiple.\n\n---\n\n## Product\n\n### ChatGPT\n| Examples                       | Capabilities                        | Limitations                        |\n|---------------------------------|-------------------------------------|------------------------------------|\n| "Explain quantum computing in simple terms" | "Remember what users said earlier in the conversation" | May occasionally generate incorrect information |\n| "What can you give me for my dad\'s birthday?" | "Allows users to follow-up questions" | Limited knowledge of world events after 2021 |\n| "How do I make an HTTP request in JavaScript?" | "Trained to provide harmless requests" |                                    |')

# Let's look at parsed first page
print(documents[0].page_content)

# OpenAI

OpenAI is an AI research laboratory.

#ai-models #ai

## Revenue
- **$1,000,000,000**  
  2023

## Valuation
- **$28,000,000,000**  
  2023

## Growth Rate (Y/Y)
- **400%**  
  2023

## Funding
- **$11,300,000,000**  
  2023

---

## Details
- **Headquarters:** San Francisco, CA
- **CEO:** Sam Altman

[Visit Website](#)

---

## Revenue
### ARR ($M)  | Growth
--- | ---
$1000M  | 456%
$750M   | 
$500M   | 
$250M   | $36M
$0     | $200M

is on track to hit $1B in annual recurring revenue by the end of 2023, up about 400% from an estimated $200M at the end of 2022.

OpenAI overall lost about $540M last year while developing ChatGPT, and those losses are expected to increase dramatically in 2023 with the growth in popularity of their consumer tools, with CEO Sam Altman remarking that OpenAI is likely to be "the most capital-intensive startup in Silicon Valley history."

The reason for that is operating ChatGPT is massively expensive. One analysis of ChatGPT put the running cost at about $700,000 per day taking into account the underlying costs of GPU hours and hardware. That amount—derived from the 175 billion parameter-large architecture of GPT-3—would be even higher with the 100 trillion parameters of GPT-4.

---

## Valuation
In April 2023, OpenAI raised its latest round of $300M at a roughly $29B valuation from Sequoia Capital, Andreessen Horowitz, Thrive and K2 Global.

Assuming OpenAI was at roughly $300M in ARR at the time, that would have given them a 96x forward revenue multiple.

---

## Product

### ChatGPT
| Examples                       | Capabilities                        | Limitations                        |
|---------------------------------|-------------------------------------|------------------------------------|
| "Explain quantum computing in simple terms" | "Remember what users said earlier in the conversation" | May occasionally generate incorrect information |
| "What can you give me for my dad's birthday?" | "Allows users to follow-up questions" | Limited knowledge of world events after 2021 |
| "How do I make an HTTP request in JavaScript?" | "Trained to provide harmless requests" |                                    |

延迟加载

加载器总是延迟获取结果。.load() 方法等同于 .lazy_load()。

API 参考

ZeroxPDFLoader

此加载器类使用文件路径和模型类型进行初始化，并通过 zerox_kwargs 支持自定义配置，以处理 Zerox 特定的参数。

参数:

• file_path (Union[str, Path]): PDF 文件的路径。
• model (str): 用于处理的具备视觉能力的模型，格式为 <provider>/<model>。有效值的一些示例如下：
  • model = "gpt-4o-mini" ## openai 模型
  • model = "azure/gpt-4o-mini"
  • model = "gemini/gpt-4o-mini"
  • model="claude-3-opus-20240229"
  • model = "vertex_ai/gemini-1.5-flash-001"
  • 更多详情请参阅 Zerox 文档
  • 默认为 "gpt-4o-mini"。
• **zerox_kwargs (dict): 其他 Zerox 特定的参数，例如 API 密钥、端点等。
  • 请参阅 Zerox 文档

方法:

• lazy_load: 生成一个 Document 实例的迭代器，每个实例代表 PDF 的一页，并包含页面编号和来源等元数据。

完整 API 文档请参见 此处

注意事项

• 模型兼容性: Zerox 支持一系列具备视觉能力的模型。请参阅 Zerox 的 GitHub 文档，了解支持的模型列表和配置详情。
• 环境变量: 确保按照 Zerox 文档中的说明设置所需的环>境变量，例如 API_KEY 或端点详情。
• 异步处理: 如果您在 Jupyter Notebook 中遇到与事件循环相关的错误，您可能需要按照设置部分所示应用 nest_asyncio。

故障排除

• RuntimeError: 此事件循环已在运行: 在 Jupyter 等环境中，使用 nest_asyncio.apply() 以防止异步循环冲突。
• 配置错误: 验证 zerox_kwargs 是否与您所选模型的预期参数匹配，并且所有必要的环境变量均已设置。

更多资源

• Zerox 文档: Zerox GitHub 仓库
• LangChain 文档加载器: LangChain 文档

相关

• 文档加载器 概念指南
• 文档加载器 操作指南