Usage

Conversion

Convert a single document

To convert invidual PDF documents, use convert(), for example:

from docling.document_converter import DocumentConverter

source = "https://arxiv.org/pdf/2408.09869"  # PDF path or URL
converter = DocumentConverter()
result = converter.convert(source)
print(result.document.export_to_markdown())  # output: "### Docling Technical Report[...]"

CLI

You can also use Docling directly from your command line to convert individual files —be it local or by URL— or whole directories.

A simple example would look like this:

docling https://arxiv.org/pdf/2206.01062

To see all available options (export formats etc.) run docling --help.

CLI reference

Here are the available options as of this writing (for an up-to-date listing, run `docling --help`): ```console $ docling --help Usage: docling [OPTIONS] source ╭─ Arguments ───────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ │ * input_sources source PDF files to convert. Can be local file / directory paths or URL. [default: None] │ │ [required] │ ╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ ╭─ Options ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ │ --from [docx|pptx|html|image|pdf] Specify input formats to convert from. │ │ Defaults to all formats. │ │ [default: None] │ │ --to [md|json|text|doctags] Specify output formats. Defaults to │ │ Markdown. │ │ [default: None] │ │ --ocr --no-ocr If enabled, the bitmap content will be │ │ processed using OCR. │ │ [default: ocr] │ │ --ocr-engine [easyocr|tesseract_cli|tesseract] The OCR engine to use. [default: easyocr] │ │ --abort-on-error --no-abort-on-error If enabled, the bitmap content will be │ │ processed using OCR. │ │ [default: no-abort-on-error] │ │ --output PATH Output directory where results are saved. │ │ [default: .] │ │ --version Show version information. │ │ --help Show this message and exit. │ ╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ ```

Advanced options

Adjust pipeline features

The example file custom_convert.py contains multiple ways one can adjust the conversion pipeline and features.

Control PDF table extraction options

You can control if table structure recognition should map the recognized structure back to PDF cells (default) or use text cells from the structure prediction itself. This can improve output quality if you find that multiple columns in extracted tables are erroneously merged into one.

from docling.datamodel.base_models import InputFormat
from docling.document_converter import DocumentConverter, PdfFormatOption
from docling.datamodel.pipeline_options import PdfPipelineOptions

pipeline_options = PdfPipelineOptions(do_table_structure=True)
pipeline_options.table_structure_options.do_cell_matching = False  # uses text cells predicted from table structure model

doc_converter = DocumentConverter(
    format_options={
        InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
    }
)

Since docling 1.16.0: You can control which TableFormer mode you want to use. Choose between TableFormerMode.FAST (default) and TableFormerMode.ACCURATE (better, but slower) to receive better quality with difficult table structures.

from docling.datamodel.base_models import InputFormat
from docling.document_converter import DocumentConverter, PdfFormatOption
from docling.datamodel.pipeline_options import PdfPipelineOptions, TableFormerMode

pipeline_options = PdfPipelineOptions(do_table_structure=True)
pipeline_options.table_structure_options.mode = TableFormerMode.ACCURATE  # use more accurate TableFormer model

doc_converter = DocumentConverter(
    format_options={
        InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
    }
)

Impose limits on the document size

You can limit the file size and number of pages which should be allowed to process per document:

from pathlib import Path
from docling.document_converter import DocumentConverter

source = "https://arxiv.org/pdf/2408.09869"
converter = DocumentConverter()
result = converter.convert(source, max_num_pages=100, max_file_size=20971520)

Convert from binary PDF streams

You can convert PDFs from a binary stream instead of from the filesystem as follows:

from io import BytesIO
from docling.datamodel.base_models import DocumentStream
from docling.document_converter import DocumentConverter

buf = BytesIO(your_binary_stream)
source = DocumentStream(filename="my_doc.pdf", stream=buf)
converter = DocumentConverter()
result = converter.convert(source)

Limit resource usage

You can limit the CPU threads used by Docling by setting the environment variable OMP_NUM_THREADS accordingly. The default setting is using 4 CPU threads.

Chunking

You can perform a hierarchy-aware chunking of a Docling document as follows:

from docling.document_converter import DocumentConverter
from docling_core.transforms.chunker import HierarchicalChunker

conv_res = DocumentConverter().convert("https://arxiv.org/pdf/2206.01062")
doc = conv_res.document
chunks = list(HierarchicalChunker().chunk(doc))

print(chunks[30])
# {
#   "text": "Lately, new types of ML models for document-layout analysis have emerged [...]",
#   "meta": {
#     "doc_items": [{
#       "self_ref": "#/texts/40",
#       "label": "text",
#       "prov": [{
#         "page_no": 2,
#         "bbox": {"l": 317.06, "t": 325.81, "r": 559.18, "b": 239.97, ...},
#       }]
#     }],
#     "headings": ["2 RELATED WORK"],
#   }
# }