Convert one or more documents with Docling

Runs Docling's document-understanding pipeline over local file paths or URLs and returns lightweight R handles around the resulting DoclingDocuments. Each handle can be exported to Markdown or JSON (as_markdown(), as_json()), mined for tables (docling_tables()), or split into chunks for RAG (docling_chunk()).

Usage

docling_convert(
  source,
  ocr = TRUE,
  table_mode = c("accurate", "fast"),
  device = c("auto", "cpu", "cuda", "mps"),
  num_threads = NULL,
  images = FALSE,
  images_scale = 1,
  ...
)

Arguments

source

A character vector of file paths and/or URLs. A single source returns one docling_document; multiple sources are converted in one batch and returned as a docling_document_list.

ocr

Logical; run OCR on the document. Defaults to TRUE. Set to FALSE to skip OCR for faster conversion of born-digital documents.

table_mode

Table-structure model mode, one of "fast" or "accurate" (default). "accurate" produces better table structure at some speed cost.

device

Accelerator device for the deep-learning models: "auto" (default), "cpu", "cuda", or "mps".

num_threads

Optional integer number of CPU threads for the accelerator. NULL (default) leaves Docling's default.

images

Logical; generate and retain page and picture images so they can be saved later with docling_figures(). Defaults to FALSE (smaller, faster results). Required if you want image files out.

images_scale

Image resolution scale relative to 72 DPI when images is TRUE (for example 2 is approx. 144 DPI). Defaults to 1.

...

Reserved for future pipeline options; currently ignored with a warning if supplied.

Value

For a single source, an object of class docling_document (a list with the underlying Python document and the original source). For multiple sources, a docling_document_list: a list of docling_document objects named by source.

Details

Supported inputs include PDF, DOCX, PPTX, XLSX, HTML, Markdown, AsciiDoc and common image formats, as determined by the installed Docling version.

See also

as_markdown(), docling_tables(), docling_chunk()

Examples

if (FALSE) { # \dontrun{
doc <- docling_convert("https://arxiv.org/pdf/2408.09869")
as_markdown(doc)

# Batch, OCR off, fast tables
docs <- docling_convert(c("a.pdf", "b.pdf"), ocr = FALSE, table_mode = "fast")
} # }