Imported from VectorSpaceLab/AREX-Skill (
skills/repositories/repo-skills/docling/sub-skills/extraction/SKILL.md). Install upstream withnpx skills add VectorSpaceLab/AREX-Skill --skill extraction. Copyright stays with the author (MIT).
Docling Structured Extraction
Use this sub-skill when the user wants structured field extraction from PDF or image documents with DocumentExtractor, extraction templates, and page-level extraction results.
Route elsewhere when the task is primarily:
- General document conversion, parsing, or
DoclingDocumentcreation: use theconversionsub-skill. - Output serialization to Markdown, JSON, YAML, HTML, text, doctags, VTT, or Docling JSON: use the
document-outputssub-skill. - VLM backend selection, accelerator configuration, remote service wiring, or model/download strategy: use the
advanced-pipelinessub-skill.
Quick Use
- Install an extraction-capable Docling package variant before runtime extraction; VLM extraction needs model/backend dependencies beyond a minimal import-only install.
- Start with
DocumentExtractor(allowed_formats=[InputFormat.IMAGE, InputFormat.PDF])because default extraction backends are configured for images and PDFs. - Pass a template as a JSON-like string,
dict, Pydantic model instance, or Pydantic model class. - Validate
result.pages[*].extracted_datawith your own Pydantic model before trusting fields downstream. - Keep fixture runs explicit and local; do not assume model artifacts are already downloaded unless the environment owner has prepared them.
References
references/api-reference.mdcoversDocumentExtractor, extraction options, template types, and result models.references/workflows.mdprovides recipes for small field extraction, Pydantic validation, and safe fixture runs.references/troubleshooting.mdlists common failures including unsupported formats, missing model extras, template mismatches, and remote opt-in.scripts/extract_fixture.pyis a safe helper for local image/PDF fixture extraction; it prints help and never downloads by default.