> ## Documentation Index
> Fetch the complete documentation index at: https://nvd-54.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# PDFPlumber 集成

> 使用 LangChain Python 集成 PDFPlumber 文档加载器。

Like PyMuPDF, the output Documents contain detailed metadata about the PDF and its pages, and returns one document per page.

## 概述

### 集成详情

| Class                                                                                                                  | Package                                                                             | Local | Serializable | JS support |
| :--------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------- | :---: | :----------: | :--------: |
| [`PDFPlumberLoader`](https://reference.langchain.com/python/langchain-community/document_loaders/pdf/PDFPlumberLoader) | [`langchain-community`](https://reference.langchain.com/python/langchain-community) |   ✅   |       ❌      |      ❌     |

### 加载器特性

|       Source       | Document Lazy Loading | Native Async Support |
| :----------------: | :-------------------: | :------------------: |
| `PDFPlumberLoader` |           ✅           |           ❌          |

## 设置

### 凭证

使用此加载器不需要凭证。

要启用模型调用的自动追踪，请设置你的 [LangSmith](/langsmith/home) API 密钥：

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
os.environ["LANGSMITH_TRACING"] = "true"
```

### 安装

安装 **langchain-community**。

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pip install -qU langchain-community pdfplumber
```

## 初始化

现在我们可以实例化模型对象并加载文档：

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_community.document_loaders import PDFPlumberLoader

loader = PDFPlumberLoader("./example_data/layout-parser-paper.pdf")
```

## 加载

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
docs = loader.load()
docs[0]
```

```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
Document(metadata={'source': './example_data/layout-parser-paper.pdf', 'file_path': './example_data/layout-parser-paper.pdf', 'page': 0, 'total_pages': 16, 'Author': '', 'CreationDate': 'D:20210622012710Z', 'Creator': 'LaTeX with hyperref', 'Keywords': '', 'ModDate': 'D:20210622012710Z', 'PTEX.Fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'Producer': 'pdfTeX-1.40.21', 'Subject': '', 'Title': '', 'Trapped': 'False'}, page_content='LayoutParser: A Unified Toolkit for Deep\nLearning Based Document Image Analysis\nZejiang Shen1 ((cid:0)), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\nLee4, Jacob Carlson3, and Weining Li5\n1 Allen Institute for AI\nshannons@allenai.org\n2 Brown University\nruochen zhang@brown.edu\n3 Harvard University\n{melissadell,jacob carlson}@fas.harvard.edu\n4 University of Washington\nbcgl@cs.washington.edu\n5 University of Waterloo\nw422li@uwaterloo.ca\nAbstract. Recentadvancesindocumentimageanalysis(DIA)havebeen\nprimarily driven by the application of neural networks. Ideally, research\noutcomescouldbeeasilydeployedinproductionandextendedforfurther\ninvestigation. However, various factors like loosely organized codebases\nand sophisticated model configurations complicate the easy reuse of im-\nportantinnovationsbyawideaudience.Thoughtherehavebeenon-going\nefforts to improve reusability and simplify deep learning (DL) model\ndevelopmentindisciplineslikenaturallanguageprocessingandcomputer\nvision, none of them are optimized for challenges in the domain of DIA.\nThis represents a major gap in the existing toolkit, as DIA is central to\nacademicresearchacross awiderangeof disciplinesinthesocialsciences\nand humanities. This paper introduces LayoutParser, an open-source\nlibrary for streamlining the usage of DL in DIA research and applica-\ntions. The core LayoutParser library comes with a set of simple and\nintuitiveinterfacesforapplyingandcustomizingDLmodelsforlayoutde-\ntection,characterrecognition,andmanyotherdocumentprocessingtasks.\nTo promote extensibility, LayoutParser also incorporates a community\nplatform for sharing both pre-trained models and full document digiti-\nzation pipelines. We demonstrate that LayoutParser is helpful for both\nlightweight and large-scale digitization pipelines in real-word use cases.\nThe library is publicly available at https://layout-parser.github.io.\nKeywords: DocumentImageAnalysis·DeepLearning·LayoutAnalysis\n· Character Recognition · Open Source library · Toolkit.\n1 Introduction\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\ndocumentimageanalysis(DIA)tasksincludingdocumentimageclassification[11,\n1202\nnuJ\n12\n]VC.sc[\n2v84351.3012:viXra\n')
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
print(docs[0].metadata)
```

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
{'source': './example_data/layout-parser-paper.pdf', 'file_path': './example_data/layout-parser-paper.pdf', 'page': 0, 'total_pages': 16, 'Author': '', 'CreationDate': 'D:20210622012710Z', 'Creator': 'LaTeX with hyperref', 'Keywords': '', 'ModDate': 'D:20210622012710Z', 'PTEX.Fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'Producer': 'pdfTeX-1.40.21', 'Subject': '', 'Title': '', 'Trapped': 'False'}
```

## 惰性加载

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
page = []
for doc in loader.lazy_load():
    page.append(doc)
    if len(page) >= 10:
        # do some paged operation, e.g.
        # index.upsert(page)

        page = []
```

***

## API 参考

For detailed documentation of all `PDFPlumberLoader` features and configurations head to the [API reference](https://reference.langchain.com/python/langchain-community/document_loaders/pdf/PDFPlumberLoader)

***

<div className="source-links">
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    [通过 MCP 将这些文档连接到](/use-these-docs) Claude、VSCode 等工具以获取实时答案。
  </Callout>

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    [在 GitHub 上编辑此页面](https://github.com/langchain-ai/docs/edit/main/src/oss/python/integrations/document_loaders/pdfplumber.mdx) 或 [提交 issue](https://github.com/langchain-ai/docs/issues/new/choose)。
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