docling

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/ # → /tmp/report.md 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 vs native), 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 ).