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docling

docling

Use Docling to convert, parse, configure, extract, chunk, serve, and maintain document-processing workflows across Python APIs, CLI commands, optional pipelines, and repository edits.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill 문서

GitHub

name: docling description: "Use Docling to convert, parse, configure, extract, chunk, serve, and maintain document-processing workflows across Python APIs, CLI commands, optional pipelines, and repository edits." disable-model-invocation: true metadata: disco-role: operating license: MIT

Docling

Use this skill when a task involves Docling, the Python SDK and CLI for converting PDFs, Office documents, HTML, Markdown, images, audio/video, XML, CSV, and other sources into DoclingDocument for downstream AI workflows.

Start Here

  1. Read references/install-and-environment.md when choosing docling vs docling-slim, optional extras, Python versions, model artifacts, or safe environment checks.
  2. Read references/repo-provenance.md before deciding whether this skill matches a current checkout or should be refreshed.
  3. Read references/troubleshooting.md for cross-cutting install/import, optional dependency, model download, CLI/API, remote-service, and repository-edit failures.
  4. Use the sub-skill route map below; each sub-skill owns its nearby references and scripts.

Route Map

User taskUse sub-skillWhy
Convert a local file, URL, binary stream, or in-memory Markdown/HTML/DocLang string with Pythonsub-skills/conversion/SKILL.mdOwns DocumentConverter, convert, convert_all, convert_string, DocumentStream, limits, headers, and conversion failures.
Build or explain docling local CLI commands and supported formatssub-skills/cli-and-formats/SKILL.mdOwns docling convert, --from, --to, output directories, image export/fetch modes, and command construction.
Tune the standard PDF pipeline, OCR, table structure, accelerators, artifacts, and option modelssub-skills/pipeline-configuration/SKILL.mdOwns PdfPipelineOptions, OCR/table options, artifacts_path, AcceleratorOptions, and option validation.
Export, serialize, chunk, or post-process an existing DoclingDocumentsub-skills/document-outputs/SKILL.mdOwns Markdown/JSON/YAML/HTML/Text/DocTags/WebVTT exports, image modes, table extraction, and RAG chunking.
Use VLM, ASR/audio-video, enrichments, advanced model backends, GPU/MLX, or offline model prefetchsub-skills/advanced-pipelines/SKILL.mdOwns VlmPipeline, AsrPipeline, enrichment flags, model catalog decisions, optional extras, and no-download preflights.
Call a running docling-serve endpoint or use docling convert-remote/service client SDKsub-skills/remote-service-client/SKILL.mdOwns service URL/API key handling, remote CLI, sync/async client, batches, jobs, chunk endpoints, and network-safe checks.
Extract structured fields from documentssub-skills/extraction/SKILL.mdOwns DocumentExtractor, extraction templates/options/results, and VLM extraction caveats.
Modify the Docling repository itselfsub-skills/repo-maintenance/SKILL.mdOwns package layout, extras, CLI/docs generation, tests, validation, and repository-specific agent rules.

Public Package Facts

  • Install the full user package with pip install docling for typical users; install docling-slim with extras only when intentionally composing a smaller environment.
  • Python support in this snapshot is >=3.10,<4.0.
  • The docling CLI exposes local convert behavior and remote convert-remote behavior; bare docling SOURCE routes to local conversion.
  • docling-tools models download prefetches model artifacts for offline/local use, but it can download large files; do not run it unless the user wants model downloads.
  • Core Python APIs include DocumentConverter, DocumentExtractor, InputFormat, OutputFormat, pipeline option classes, HybridChunker, and DoclingServiceClient.

Safety Defaults

  • Do not run model downloads, VLM/ASR inference, remote service calls, network document fetches, or GPU-heavy examples unless the user asks or the workflow explicitly requires them.
  • Prefer no-network helpers first: command builders, config preflights, import checks, --help, and tiny local fixtures.
  • Treat original repository docs, examples, tests, and scripts as evidence only; this skill's runtime instructions and helpers are bundled under this directory.
  • For remote model APIs inside a local pipeline, require enable_remote_services=True; for docling-serve conversion, route to remote-service-client.
  • For repository code changes, obey AGENTS.md, run focused tests for touched behavior, and run make validate before considering the change complete when feasible.

Bundled Shared Helpers

  • scripts/check_docling_environment.py checks package importability, CLI availability, optional modules, and common external binaries without converting documents or downloading models.

Common First Moves

  • Python conversion: route to conversion, then use document-outputs if the user needs Markdown/JSON/HTML/chunking/table files.
  • CLI conversion: route to cli-and-formats, then use pipeline-configuration for OCR/table/model flags or advanced-pipelines for VLM/ASR/enrichment choices.
  • Remote service: route directly to remote-service-client; do not upload documents during preflight unless the user authorizes a live service call.
  • Current-checkout edits: route to repo-maintenance; do not use public runtime examples as a substitute for repository tests.
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