docling
CommunityUnlock document data, power your RAG pipelines.
Data & Analytics#ocr#markdown#pdf extraction#chunking#data preparation#document parsing#rag pipeline
Authoranderskev
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Extracting structured information from diverse document formats (PDFs, DOCX, images) for AI applications like RAG is challenging due to layout complexities, OCR needs, and varied content types. Docling simplifies this by providing a unified parsing solution.
Core Features & Use Cases
- Multi-Format Parsing: Convert PDFs, Word, PowerPoint, HTML, and images into structured
DoclingDocumentobjects. - Advanced Data Extraction: Extract text, tables, and images with layout understanding, including OCR for scanned documents.
- RAG-Ready Chunking: Generate context-rich chunks with hierarchical metadata, optimized for vector databases and retrieval.
- Use Case: Process a folder of mixed legal documents (scanned PDFs, DOCX contracts) to extract key clauses and tables, then chunk them for a RAG system to answer specific legal questions.
Quick Start
Convert the attached 'report.pdf' into Markdown format, ensuring OCR is enabled for any scanned text.
Dependency Matrix
Required Modules
None requiredComponents
references
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: docling Download link: https://github.com/anderskev/amelia/archive/main.zip#docling Please download this .zip file, extract it, and install it in the .claude/skills/ directory.