跳至主要內容

PyPDFDirectoryLoader

此載入器會從特定目錄載入所有 PDF 檔案。

概觀​

整合細節​

類別套件本地可序列化JS 支援
PyPDFDirectoryLoaderlangchain_community✅❌❌

載入器功能​

來源文件延遲載入原生非同步支援
PyPDFDirectoryLoader✅❌

設定​

憑證​

此載入器不需要憑證。

如果您想要取得模型呼叫的自動化最佳追蹤，您也可以透過取消註解下方內容來設定您的 LangSmith API 金鑰

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

安裝​

安裝 langchain_community。

%pip install -qU langchain_community

初始化​

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 PyPDFDirectoryLoader

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

載入​

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​

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 參考​

如需所有 PyPDFDirectoryLoader 功能和設定的詳細文件，請前往 API 參考： https://langchain-python.dev.org.tw/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFDirectoryLoader.html

此頁面是否有幫助？