> ## Documentation Index > Fetch the complete documentation index at: https://langchain-zh.cn/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。 与 PyMuPDF 类似，输出的 Document 对象包含 PDF 及其页面的详细元数据，并会为每一页返回一个文档。 ## 概述 ### 集成详情 | 类 | 包 | 本地 | 可序列化 | JS 支持 | | :--------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------- | :-: | :--: | :---: | | [`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 | 来源 | 文档惰性加载 | 原生异步支持 | | :----------------: | :----: | :----: | | `PDFPlumberLoader` | ✅ | ❌ | ## 设置 ### 凭证 使用此加载器无需任何凭证。 要启用模型调用的自动追踪，请设置您的 [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: # 执行一些分页操作，例如： # index.upsert(page) page = [] ``` *** ## API 参考 有关 `PDFPlumberLoader` 所有功能和配置的详细文档，请前往 [API 参考](https://reference.langchain.com/python/langchain-community/document_loaders/pdf/PDFPlumberLoader) ***
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