PyPDFium2Loader
此笔记本提供了有关如何开始使用 PyPDFium2 文档加载器 的快速概述。有关所有 ModuleNameLoader 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 |
|---|---|---|---|---|
| PyPDFium2Loader | langchain_community | ✅ | ❌ | ❌ |
加载器功能
| 来源 | 文档延迟加载 | 原生异步支持 |
|---|---|---|
| PyPDFium2Loader | ✅ | ❌ |
设置
要访问 PyPDFium2 文档加载器，您需要安装 langchain-community 集成包。
凭据
无需凭据。
如果您希望获得对模型调用的自动化最佳类跟踪，还可以通过取消注释以下内容来设置您的 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 PyPDFium2Loader
file_path = "./example_data/layout-parser-paper.pdf"
loader = PyPDFium2Loader(file_path)
API 参考：PyPDFium2Loader
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\r\nLearning Based Document Image Analysis\r\nZejiang Shen\r\n1\r\n(), Ruochen Zhang\r\n2\r\n, Melissa Dell\r\n3\r\n, Benjamin Charles Germain\r\nLee\r\n4\r\n, Jacob Carlson\r\n3\r\n, and Weining Li\r\n5\r\n1 Allen Institute for AI\r\[email protected] 2 Brown University\r\nruochen [email protected] 3 Harvard University\r\n{melissadell,jacob carlson}@fas.harvard.edu\r\n4 University of Washington\r\[email protected] 5 University of Waterloo\r\[email protected]\r\nAbstract. Recent advances in document image analysis (DIA) have been\r\nprimarily driven by the application of neural networks. Ideally, research\r\noutcomes could be easily deployed in production and extended for further\r\ninvestigation. However, various factors like loosely organized codebases\r\nand sophisticated model configurations complicate the easy reuse of im\x02portant innovations by a wide audience. Though there have been on-going\r\nefforts to improve reusability and simplify deep learning (DL) model\r\ndevelopment in disciplines like natural language processing and computer\r\nvision, none of them are optimized for challenges in the domain of DIA.\r\nThis represents a major gap in the existing toolkit, as DIA is central to\r\nacademic research across a wide range of disciplines in the social sciences\r\nand humanities. This paper introduces LayoutParser, an open-source\r\nlibrary for streamlining the usage of DL in DIA research and applica\x02tions. The core LayoutParser library comes with a set of simple and\r\nintuitive interfaces for applying and customizing DL models for layout de\x02tection, character recognition, and many other document processing tasks.\r\nTo promote extensibility, LayoutParser also incorporates a community\r\nplatform for sharing both pre-trained models and full document digiti\x02zation pipelines. We demonstrate that LayoutParser is helpful for both\r\nlightweight and large-scale digitization pipelines in real-word use cases.\r\nThe library is publicly available at https://layout-parser.github.io.\r\nKeywords: Document Image Analysis· Deep Learning· Layout Analysis\r\n· Character Recognition· Open Source library· Toolkit.\r\n1 Introduction\r\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\r\ndocument image analysis (DIA) tasks including document image classification [11,\r\narXiv:2103.15348v2 [cs.CV] 21 Jun 2021\n')
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 PyPDFium2Loader 功能和配置的详细文档，请访问 API 参考： https://python.langchain.ac.cn/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFium2Loader.html