PyPDFLoader

此笔记本提供了有关如何开始使用PyPDF 文档加载器 的快速概述。有关所有 DocumentLoader 功能和配置的详细文档，请访问 API 参考。

概述

集成详情

类	包	本地	可序列化	JS 支持
PyPDFLoader	langchain_community	✅	❌	❌

加载器功能

来源	文档延迟加载	原生异步支持
PyPDFLoader	✅	❌

设置

凭据

使用PyPDFLoader不需要任何凭据。

安装

要使用PyPDFLoader，您需要下载langchain-community Python 包。

%pip install -qU langchain_community pypdf

初始化

现在我们可以实例化我们的模型对象并加载文档。

from langchain_community.document_loaders import PyPDFLoader

loader = PyPDFLoader(
    "./example_data/layout-parser-paper.pdf",
)

API 参考：PyPDFLoader

加载

docs = loader.load()
docs[0]

Document(metadata={'source': './example_data/layout-parser-paper.pdf', 'page': 0}, 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\n1Allen Institute for AI\[email protected]\n2Brown University\nruochen [email protected]\n3Harvard University\n{melissadell,jacob carlson }@fas.harvard.edu\n4University of Washington\[email protected]\n5University of Waterloo\[email protected]\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,arXiv:2103.15348v2  [cs.CV]  21 Jun 2021')

print(docs[0].metadata)

{'source': './example_data/layout-parser-paper.pdf', 'page': 0}

延迟加载

pages = []
for doc in loader.lazy_load():
    pages.append(doc)
    if len(pages) >= 10:
        # do some paged operation, e.g.
        # index.upsert(page)

        pages = []
len(pages)

6

print(pages[0].page_content[:100])
print(pages[0].metadata)

LayoutParser : A Unified Toolkit for DL-Based DIA 11
focuses on precision, efficiency, and robustness. 
{'source': './example_data/layout-parser-paper.pdf', 'page': 10}

API 参考

有关所有PyPDFLoader功能和配置的详细文档，请访问 API 参考： https://python.langchain.ac.cn/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFLoader.html

相关

• 文档加载器 概念指南
• 文档加载器 操作指南