PyPDFLoader
本筆記提供快速入門 PyPDF 文件載入器 的概觀。如需所有 DocumentLoader 功能和組態的詳細文件，請前往 API 參考。
概觀 (Overview)
整合詳細資訊 (Integration details)
| 類別 (Class) | 套件 (Package) | 本地 (Local) | 可序列化 (Serializable) | JS 支援 (JS support) |
|---|---|---|---|---|
| PyPDFLoader | langchain_community | ✅ | ❌ | ❌ |
載入器功能 (Loader features)
| 來源 (Source) | 文件延遲載入 (Document Lazy Loading) | 原生非同步支援 (Native Async Support) |
|---|---|---|
| PyPDFLoader | ✅ | ❌ |
設定 (Setup)
憑證 (Credentials)
使用 PyPDFLoader 不需要憑證。
安裝 (Installation)
若要使用 PyPDFLoader，您需要下載 langchain-community python 套件
%pip install -qU langchain_community pypdf
初始化 (Initialization)
現在我們可以實例化我們的模型物件並載入文件
from langchain_community.document_loaders import PyPDFLoader
loader = PyPDFLoader(
"./example_data/layout-parser-paper.pdf",
)
API 參考：PyPDFLoader
載入 (Load)
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\nshannons@allenai.org\n2Brown University\nruochen zhang@brown.edu\n3Harvard University\n{melissadell,jacob carlson }@fas.harvard.edu\n4University of Washington\nbcgl@cs.washington.edu\n5University 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,arXiv:2103.15348v2 [cs.CV] 21 Jun 2021')
print(docs[0].metadata)
{'source': './example_data/layout-parser-paper.pdf', 'page': 0}
延遲載入 (Lazy Load)
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 參考 (API reference)
如需所有 PyPDFLoader 功能和組態的詳細文件，請前往 API 參考： https://langchain-python.dev.org.tw/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFLoader.html