| name | docling |
| description | Use Docling to understand the content of documents in any supported format — PDF (born-digital or scanned), DOCX, PPTX, XLSX, HTML, Markdown, AsciiDoc, CSV, images, audio, and XML — by converting them into a unified DoclingDocument (Markdown or structured JSON). Use this skill whenever you need to read, parse, convert, extract, or chunk a document you cannot read directly: "what's in this PDF", "convert this to markdown", "extract the tables", "chunk this for RAG", "read this scanned document", "parse this DOCX/PPTX". Covers the `docling` CLI, the Python SDK (DocumentConverter + PipelineOptions), the remote Service Client (self-hosted or managed docling-serve), and the docling-slim install extras for a minimal dependency footprint.
|
| license | MIT |
| compatibility | Requires Python 3.10+ |
| metadata | {"author":"docling-project","version":"1.0","upstream":"https://github.com/docling-project/docling"} |
| allowed-tools | Bash(docling:*) Bash(docling-tools:*) Bash(python3:*) Bash(python:*) Bash(uvx:*) Bash(uv:*) Bash(pip:*) |
Docling
Docling converts documents — PDF, DOCX, PPTX, XLSX, HTML, Markdown, AsciiDoc,
CSV, images, audio, and XML — into a single unified representation, the
DoclingDocument, which you can export as Markdown (human-readable) or
JSON (structured, lossless). Reach for Docling whenever you need to
understand the content of a file you cannot read directly, especially PDFs
(including scanned ones, via OCR or a vision-language model).
The fastest thing that works: the CLI
If you just need to read a document's content, run the CLI. It is installed with
the docling package and accepts a local path or a URL:
docling report.pdf --to md --output /tmp/
docling https://example.com/paper.pdf --to json --output /tmp/
Output files are named after the input (report.pdf → report.md). Default
output directory is the current directory. This handles the majority of
"what's in this file" requests. See references/cli.md
for pipelines (standard vs VLM), OCR engines, tables, scanned PDFs, passwords,
and every flag.
Choosing how to use Docling
| You need to… | Use | Reference |
|---|
| Read / convert a file once, from the shell | CLI (docling …) | references/cli.md |
| Convert programmatically, tune the pipeline, batch, ASR, export images/tables | Python SDK (DocumentConverter + PipelineOptions) | references/python-sdk.md |
| Pull specific typed fields out of a document (not the whole doc) | DocumentExtractor (structured extraction, beta) | references/extraction.md |
| Chunk documents for retrieval / feed a RAG index | Chunking + framework loaders | references/rag.md |
| Offload conversion to a remote service — low latency, scalable, no local ML deps or GPU | Service Client (self-hosted or managed docling-serve) | references/service-client.md |
| Install only the dependencies you actually use | docling-slim extras | references/slim-packaging.md |
Rules of thumb:
- Local, one-off, no code → CLI.
- Custom pipeline, chunking, structure analysis, embedding in an app → Python SDK.
- Many documents, low-latency, no GPU/ML install to manage, scale on demand
→ Service Client against a
docling-serve endpoint (self-hosted or the
managed Docling for IBM watsonx service).
- Minimize install size / avoid pulling torch and OCR engines you don't need
→ docling-slim with targeted extras.
Running without installing (uvx)
You can run the CLI without a persistent install:
uvx --from docling docling report.pdf --to md --output /tmp/
Output conventions
- Always report the conversion status and (for PDFs) the page count.
- If the user does not specify a format, ask whether they want Markdown (readable) or JSON / DoclingDocument (structured, lossless).
- For tables, prefer
export_to_markdown() / export_to_dataframe() on the table item (Python) — see references/python-sdk.md.
- If a converted PDF comes back near-empty, repeated, or full of
�, the source is likely scanned or complex layout — retry with OCR or --pipeline vlm (see references/cli.md).
