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PyMuPDF

PyMuPDF 針對速度進行了最佳化，並包含關於 PDF 及其頁面的詳細元數據。它為每個頁面返回一個文檔。

概觀​

整合細節​

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

載入器功能​

來源文件延遲載入原生非同步支援
PyMuPDFLoader✅❌

設定​

憑證​

使用 PyMuPDFLoader 不需要憑證。

如果您想要獲得一流的模型呼叫自動追蹤，您還可以取消註釋下面的內容，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 和 pymupdf。

%pip install -qU langchain-community pymupdf

初始化​

現在我們可以初始化我們的載入器並開始載入文檔。

from langchain_community.document_loaders import PyMuPDFLoader

loader = PyMuPDFLoader("./example_data/layout-parser-paper.pdf")
API 參考：PyMuPDFLoader

載入​

您可以將 PyMuPDF 文檔中的任何選項作為關鍵字參數傳遞給 load 呼叫，它將傳遞給 get_text() 呼叫。

docs = loader.load()
docs[0]
Document(metadata={'source': './example_data/layout-parser-paper.pdf', 'file_path': './example_data/layout-parser-paper.pdf', 'page': 0, 'total_pages': 16, 'format': 'PDF 1.5', 'title': '', 'author': '', 'subject': '', 'keywords': '', 'creator': 'LaTeX with hyperref', 'producer': 'pdfTeX-1.40.21', 'creationDate': 'D:20210622012710Z', 'modDate': 'D:20210622012710Z', 'trapped': ''}, 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\n1 Allen Institute for AI\nshannons@allenai.org\n2 Brown University\nruochen zhang@brown.edu\n3 Harvard University\n{melissadell,jacob carlson}@fas.harvard.edu\n4 University of Washington\nbcgl@cs.washington.edu\n5 University 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\nIntroduction\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,\narXiv:2103.15348v2  [cs.CV]  21 Jun 2021\n')
print(docs[0].metadata)
{'source': './example_data/layout-parser-paper.pdf', 'file_path': './example_data/layout-parser-paper.pdf', 'page': 0, 'total_pages': 16, 'format': 'PDF 1.5', 'title': '', 'author': '', 'subject': '', 'keywords': '', 'creator': 'LaTeX with hyperref', 'producer': 'pdfTeX-1.40.21', 'creationDate': 'D:20210622012710Z', 'modDate': 'D:20210622012710Z', 'trapped': ''}

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​

有關所有 PyMuPDFLoader 功能和配置的詳細文檔，請前往 API 參考： https://langchain-python.dev.org.tw/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyMuPDFLoader.html

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