{
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  "Package": "doclingr",
  "Title": "Document Intelligence via 'Docling'",
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  "Authors@R": "c(\nperson(\"André\", \"Leite\", email = \"leite@castlab.org\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-4718-9766\")),\nperson(\"Marcos\", \"Wasiliew\", email = \"marcos.wasiliew@gmail.com\", role = \"aut\",\ncomment = c(ORCID = \"0009-0004-4694-3159\")),\nperson(\"Hugo\", \"Vasconcelos\", email = \"hugo.vasconcelos@ufpe.br\", role = \"aut\",\ncomment = c(ORCID = \"0000-0001-6249-0920\")),\nperson(\"Carlos\", \"Amorim\", email = \"carlos.agaf@ufpe.br\", role = \"aut\",\ncomment = c(ORCID = \"0000-0001-6315-8305\")),\nperson(\"Diogo\", \"Bezerra\", email = \"diogo.bezerra@ufpe.br\", role = \"aut\",\ncomment = c(ORCID = \"0000-0002-1216-8674\")),\nperson(\"Júlia\", \"Nascimento Barreto\", email = \"juliabarreto@gd.seplag.pe.gov.br\", role = \"aut\",\ncomment = c(ORCID = \"0009-0004-2851-7770\")))",
  "Description": "An interface to 'Docling', a document-understanding\nlibrary that converts 'PDF', 'DOCX', 'PPTX', 'HTML' and image\ndocuments into structured, AI-ready data. The package wraps the\n'Docling' 'Python' package through 'reticulate' to extract\nlayout-aware text, tables and metadata, export to 'Markdown' or\n'JSON', and split documents into context-rich chunks suitable\nfor retrieval-augmented generation (RAG) and embedding\npipelines.",
  "License": "MIT + file LICENSE",
  "Language": "en-US",
  "Encoding": "UTF-8",
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  "BugReports": "https://github.com/StrategicProjects/doclingr/issues",
  "SystemRequirements": "Python (>= 3.9), docling (>= 2.20.0; tested with\n2.143.1)",
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  "Maintainer": "André Leite <leite@castlab.org>",
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  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2026-10-28 00:20:10 UTC",
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  "_expires": "2027-02-05T19:38:19.000Z",
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  "_published": "2026-10-28T19:38:22.717Z",
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    {
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    },
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    },
    {
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