{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/4"},{"label":"Database","url":"https://skillfed.io/packages/category/database"},{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis"}],"enrichment":{"capability":"Docling Core defines the foundational DoclingDocument data model and provides APIs for serialization, chunking, and profiling of structured document data for generative AI applications.","skillfed_tags":["document-processing","pydantic-models","generative-ai"],"use_cases":["Build a document ingestion pipeline that parses PDFs or scanned documents into a validated, queryable DoclingDocument structure for RAG systems.","Export extracted document content to multiple formats (Markdown, HTML, JSON) for downstream processing or API consumption.","Chunk large documents into semantically coherent segments for embedding and vector search in AI applications.","Analyze document collections to extract structural metrics (page counts, table distributions, image density) for quality assurance or dataset profiling.","Extend the serialization or chunking logic with custom implementations for domain-specific document processing requirements."],"what_it_does":"Docling Core is the foundational library for the Docling document processing framework. It defines DoclingDocument as a Pydantic model\u2014a strongly-typed, validated data structure for representing structured documents\u2014and provides a suite of APIs built around it. The library is designed to support generative AI workflows that need to ingest, transform, and analyze documents at scale.\n\nThe package centers on four main capabilities: the DoclingDocument model itself (with builders and exporters for formats like Markdown and HTML), a pluggable Serialization API for flexible output formats, a Chunking API for breaking documents into semantically meaningful pieces, and a Profiling API for extracting statistics and distributions from individual documents or collections. It depends on Pydantic for validation, Pillow for image handling, pandas for data operations, and several other utilities for LaTeX math, YAML, and XML processing.","worth_installing":"Yes. Docling Core is actively maintained (release 8 days old), has no known vulnerabilities, uses a permissive MIT license, and offers low install friction. It is production-stable and well-suited for teams building document-centric AI applications. Install if you are working with the Docling framework or need a structured, validated document data model with built-in serialization and chunking."},"id":"docling-core","links":{"html":"https://skillfed.io/packages/docling-core","md":"https://skillfed.io/packages/docling-core.md","pypi":"https://pypi.org/project/docling-core/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-06","license_spdx":"MIT","license_treatment":"permissive","name":"docling-core","python_support":"supports_current","summary":"A python library to define and validate data types in Docling."},"popularity":{"monthly_downloads":6339614,"position":1928,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"2.91.0"}