PyPDFDirectoryLoader

此加载器会从特定目录加载所有 PDF 文件。

概述

集成详情

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

加载器功能

来源	文档延迟加载	原生异步支持
PyPDFDirectoryLoader	✅	❌

设置

凭证

此加载器不需要任何凭证。

如果您希望获得对模型调用的最佳无缝追踪，可以通过取消下面的注释来设置您的 LangSmith API 密钥

# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"

安装

安装 langchain_community。

%pip install -qU langchain_community

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 PyPDFDirectoryLoader

directory_path = (
    "../../docs/integrations/document_loaders/example_data/layout-parser-paper.pdf"
)
loader = PyPDFDirectoryLoader("example_data/")

API 参考：PyPDFDirectoryLoader

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

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

有关所有 PyPDFDirectoryLoader 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.PyPDFDirectoryLoader.html

相关

• 文档加载器 概念指南
• 文档加载器 操作指南