> ## 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. # PyPDFDirectoryLoader 集成 > 使用 LangChain Python 集成 PyPDFDirectoryLoader 文档加载器。 此加载器从指定目录加载所有 PDF 文件。 ## 概述 ### 集成详情 | 类 | 包 | 本地 | 可序列化 | JS 支持 | | :----------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------- | :-: | :--: | :---: | | [`PyPDFDirectoryLoader`](https://reference.langchain.com/python/langchain-community/document_loaders/pdf/PyPDFDirectoryLoader) | [`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 | | :--------------------: | :----: | :----: | | `PyPDFDirectoryLoader` | ✅ | ❌ | ## 设置 ### 凭证 此加载器无需凭证。 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 pypdf pillow ``` ## 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 PyPDFDirectoryLoader directory_path = ( "../../docs/integrations/document_loaders/example_data/layout-parser-paper.pdf" ) loader = PyPDFDirectoryLoader("example_data/") ``` ## 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={'producer': 'pdfTeX-1.40.21', 'creator': 'LaTeX with hyperref', 'creationdate': '2021-06-22T01:27:10+00:00', 'author': '', 'keywords': '', 'moddate': '2021-06-22T01:27:10+00:00', 'ptex.fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'subject': '', 'title': '', 'trapped': '/False', 'source': 'example_data/layout-parser-paper.pdf', 'total_pages': 16, 'page': 0, 'page_label': '1'}, page_content='LayoutParser: A Unified Toolkit for Deep\nLearning Based Document Image Analysis\nZejiang Shen1 (\x00 ), 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. Recent advances in document image analysis (DIA) have been\nprimarily driven by the application of neural networks. Ideally, research\noutcomes could be easily deployed in production and extended for further\ninvestigation. However, various factors like loosely organized codebases\nand sophisticated model configurations complicate the easy reuse of im-\nportant innovations by a wide audience. Though there have been on-going\nefforts to improve reusability and simplify deep learning (DL) model\ndevelopment in disciplines like natural language processing and computer\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\nacademic research across a wide range of disciplines in the social sciences\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\nintuitive interfaces for applying and customizing DL models for layout de-\ntection, character recognition, and many other document processing tasks.\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: Document Image Analysis · Deep Learning · Layout Analysis\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\ndocument image analysis (DIA) tasks including document image classification [11,\narXiv:2103.15348v2 [cs.CV] 21 Jun 2021') ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} print(docs[0].metadata) ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} {'producer': 'pdfTeX-1.40.21', 'creator': 'LaTeX with hyperref', 'creationdate': '2021-06-22T01:27:10+00:00', 'author': '', 'keywords': '', 'moddate': '2021-06-22T01:27:10+00:00', 'ptex.fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'subject': '', 'title': '', 'trapped': '/False', 'source': 'example_data/layout-parser-paper.pdf', 'total_pages': 16, 'page': 0, 'page_label': '1'} ``` ## 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 `PyPDFDirectoryLoader` 所有功能和配置的详细文档，请访问 [API 参考](https://reference.langchain.com/python/langchain-community/document_loaders/pdf/PyPDFDirectoryLoader) ***
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