Docling
Docling is an open-source document processing toolkit that converts PDFs, Office files and other formats into structured information for applications and AI workflows.
Why it caught my attention
Extracting text from a PDF is relatively straightforward; preserving what that text means within the document is harder. Docling tackles that distinction, recovering tables, reading order and document structure. This makes it interesting for anyone building search, knowledge retrieval or automated document-processing workflows.
What problem does this actually solve?
Docling could be worth considering if you recognise any of these challenges.
Complex documents lose their structure
Basic extraction can flatten tables, headings and columns; Docling identifies these elements and preserves their relationships.
Scanned documents contain inaccessible text
Image-based documents cannot be searched directly; optical character recognition helps convert their contents into usable text.
AI searches lose document context
Splitting documents into arbitrary text blocks loses context; structure-aware chunks retain headings and links to original content.
Different file formats complicate processing
Separate extraction tools increase integration work; Docling provides a common document representation across multiple supported formats.
Is this a good fit for you?
A quick way to see if Docling is relevant for your situation.
- You're building AI search or retrieval systems using internal documents.
- You regularly process PDFs containing tables, diagrams or multiple columns.
- You need document conversion within private or offline environments.
- You want to automate extraction across several supported file formats.
- You're developing Python applications that need structured document content.
- You only need to read or manually edit occasional documents.
- You require a complete document management system rather than processing components.
How does it work?
In practice, the workflow looks something like this:
Install the Python package, use its command-line interface or deploy Docling Serve when applications need access through an HTTP API.
Supply a local file or supported URL, such as a PDF report, Word document or scanned image, for conversion.
Docling processes the document, identifying text, layout and supported elements such as tables, with OCR or specialised models where configured.
Convert the resulting document representation into Markdown, JSON or other supported outputs, or generate chunks that retain useful document context.
Feed the results into search indexes, AI retrieval pipelines or custom applications, retaining source references where the conversion provides them.
What else should I know?
A few important things to be aware of before you investigate further.
Open-source code and model licensing
The core project uses the MIT licence, but individual processing models may have separate licensing terms requiring review.
Local execution has resource requirements
Local processing supports offline use after model downloads, but OCR and advanced document analysis can increase processing time.
Format support has dependencies
Some formats require additional software or packages, including LibreOffice for legacy Office files and optional components for audio processing.
Extraction quality depends on content
Complex layouts, poor scans and unusual document structures may require configuration, validation or corrections before downstream use.
Think Docling could fit your environment?
Visit the Docling website to find out more, request a demo or speak to their team.
Something you'd like to know about Docling?
Is there something I haven't covered in my research? Send me your question and I'll look into it and reply personally by email.