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Feature: pdfplumber PDF loader with BaseBlobParser (#4552)
# Feature: pdfplumber PDF loader with BaseBlobParser * Adds pdfplumber as a PDF loader * Adds pdfplumber as a blob parser.
This commit is contained in:
@@ -97,7 +97,7 @@
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},
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"outputs": [
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{
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"name": "stdin",
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"OpenAI API Key: ········\n"
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@@ -673,6 +673,68 @@
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"docs = loader.load()"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "45bb0415",
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"metadata": {},
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"source": [
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"## Using pdfplumber\n",
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"\n",
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"Like PyMuPDF, the output Documents contain detailed metadata about the PDF and its pages, and returns one document per page."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "aefa758d",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.document_loaders import PDFPlumberLoader"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "049e9d9a",
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"metadata": {},
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"outputs": [],
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"source": [
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"loader = PDFPlumberLoader(\"example_data/layout-parser-paper.pdf\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "a8610efa",
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"metadata": {},
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"outputs": [],
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"source": [
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"data = loader.load()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "8132e551",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Document(page_content='LayoutParser: A Unified Toolkit for Deep\\nLearning Based Document Image Analysis\\nZejiang Shen1 ((cid:0)), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\\nLee4, Jacob Carlson3, and Weining Li5\\n1 Allen Institute for AI\\n1202 shannons@allenai.org\\n2 Brown University\\nruochen zhang@brown.edu\\n3 Harvard University\\nnuJ {melissadell,jacob carlson}@fas.harvard.edu\\n4 University of Washington\\nbcgl@cs.washington.edu\\n12 5 University of Waterloo\\nw422li@uwaterloo.ca\\n]VC.sc[\\nAbstract. Recentadvancesindocumentimageanalysis(DIA)havebeen\\nprimarily driven by the application of neural networks. Ideally, research\\noutcomescouldbeeasilydeployedinproductionandextendedforfurther\\ninvestigation. However, various factors like loosely organized codebases\\nand sophisticated model configurations complicate the easy reuse of im-\\n2v84351.3012:viXra portantinnovationsbyawideaudience.Thoughtherehavebeenon-going\\nefforts to improve reusability and simplify deep learning (DL) model\\ndevelopmentindisciplineslikenaturallanguageprocessingandcomputer\\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\\nacademicresearchacross awiderangeof disciplinesinthesocialsciences\\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\\nintuitiveinterfacesforapplyingandcustomizingDLmodelsforlayoutde-\\ntection,characterrecognition,andmanyotherdocumentprocessingtasks.\\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: DocumentImageAnalysis·DeepLearning·LayoutAnalysis\\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\\ndocumentimageanalysis(DIA)tasksincludingdocumentimageclassification[11,', metadata={'source': 'example_data/layout-parser-paper.pdf', 'file_path': 'example_data/layout-parser-paper.pdf', 'page': 1, 'total_pages': 16, 'Author': '', 'CreationDate': 'D:20210622012710Z', 'Creator': 'LaTeX with hyperref', 'Keywords': '', 'ModDate': 'D:20210622012710Z', 'PTEX.Fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'Producer': 'pdfTeX-1.40.21', 'Subject': '', 'Title': '', 'Trapped': 'False'})"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"data[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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@@ -698,7 +760,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.3"
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"version": "3.9.16"
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}
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},
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"nbformat": 4,
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