Docling - AI Data Tools Tool

Overview

Docling is an open-source tool that prepares documents and multimedia for use with generative AI models by providing configurable preprocessing pipelines. According to the project's GitHub repository (https://github.com/docling-project/docling), Docling focuses on automating common preparation steps — ingesting files, transcribing audio with models such as Whisper, cleaning and normalizing text, and producing segmented, metadata-rich outputs ready for embedding and retrieval. The project is designed to be extensible and pipeline-driven: users declare or compose preprocessing steps to match downstream needs (for example, chunking text for LLM context windows or exporting vectors for vector stores). Docling targets practical integrations with transcription models and embedding workflows so teams can move from raw documents and recordings to retrieval- and generation-ready artifacts without building each step from scratch. The GitHub repository contains source code, configuration examples, and usage notes for getting started.

Installation

Install via pip:

git clone https://github.com/docling-project/docling.git

cd docling

python -m venv .venv && source .venv/bin/activate

pip install -e .

docling --help

Key Features

• Pipeline-driven preprocessing: chain ingest, transform, and export steps for a consistent workflow.
• Audio transcription support using Whisper-style models for converting speech to text.
• Document chunking and text normalization to prepare inputs for LLM context windows.
• Export-ready outputs compatible with embedding and vector-store ingestion workflows.
• Configurable YAML-based jobs and extension hooks for custom processors and integrations.

Community

Docling is developed publicly on GitHub (https://github.com/docling-project/docling). The repository hosts source code, examples, and an issues/PR workflow for contribution. Community engagement is managed via the project's issue tracker and pull requests; interested users and contributors can open issues, propose fixes, or add processors. For up-to-date activity, contributors should consult the repository's commits, open issues, and README examples.

Key Information

• Category: Data Tools
• Type: AI Data Tools Tool