docling_chunk() gains a doc_items
list-column with per-item provenance: self_ref,
label, page and a 1-based inclusive character
span (char_start/char_end) locating each
occurrence within the originating item’s text (populated by layout
analysis; NA for formats without provenance, such as
Markdown sources).docling_write_chunks() persists chunk tibbles —
including embedding and other list-columns — to Parquet or
Feather via the arrow package (new optional dependency in Suggests), for
hand-off to Python, DuckDB and other Arrow-aware RAG tooling.vignette("options") gains a “Model cache and offline
use” section covering HF_HOME and
HF_HUB_OFFLINE for air-gapped machines.First release: an R interface to Docling for document intelligence and RAG.
docling_convert() converts a file path or URL (or a
vector of them) into a docling_document, with pipeline
options for OCR, table mode, accelerator device/threads and image
generation.as_markdown(), as_text(),
as_html(), as_json() and
as_doctags().docling_tables() returns detected tables as a list of
tibbles.docling_figures() returns figure captions/pages and can
save images; docling_n_pages() reports the page count.docling_chunk() produces RAG-ready chunks as a tidy
tibble, with hybrid and hierarchical chunkers,
tokenizer/max_tokens control, and pages/
n_doc_items metadata.docling_embed() attaches embeddings from any
user-supplied embedding function, with batching.install_docling() / docling_available()
manage the Python backend.