This commit completely revamps the Actor implementation with two major improvements: 1) CRITICAL CHANGE: Switch to official docling-serve image * Now using quay.io/ds4sd/docling-serve-cpu:latest as base image * Eliminates need for custom docling installation * Ensures compatibility with latest docling-serve features * Provides more reliable and consistent document processing 2) Fix Apify Actor KVS storage issues: * Standardize key names to follow Apify conventions: - Change "OUTPUT_RESULT" to "OUTPUT" - Change "DOCLING_LOG" to "LOG" * Add proper multi-stage Docker build: - First stage builds dependencies including apify-cli - Second stage uses official image and adds only necessary tools * Fix permission issues in Docker container: - Set up proper user and directory permissions - Create writable directories for temporary files and models - Configure environment variables for proper execution 3) Solve EACCES permission errors during CLI version checks: * Create temporary HOME directory with proper write permissions * Set APIFY_DISABLE_VERSION_CHECK=1 environment variable * Add NODE_OPTIONS="--no-warnings" to suppress update checks * Support --no-update-notifier CLI flag when available 4) Improve code organization and reliability: * Create reusable upload_to_kvs() function for all KVS operations * Ensure log files are uploaded before tools directory is removed * Set proper MIME types based on output format * Add detailed error reporting and proper cleanup * Display final output URLs for easy verification This major refactoring significantly improves reliability and maintainability by leveraging the official docling-serve image while solving persistent permission and storage issues. The Actor now properly follows Apify standards while providing a more robust document processing pipeline. Signed-off-by: Václav Vančura <commit@vancura.dev> |
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docling | ||
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CHANGELOG.md | ||
CITATION.cff | ||
CODE_OF_CONDUCT.md | ||
CONTRIBUTING.md | ||
Dockerfile | ||
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MAINTAINERS.md | ||
mkdocs.yml | ||
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README.md |
Docling
Docling parses documents and exports them to the desired format with ease and speed.
Features
- 🗂️ Reads popular document formats (PDF, DOCX, PPTX, XLSX, Images, HTML, AsciiDoc & Markdown) and exports to HTML, Markdown and JSON (with embedded and referenced images)
- 📑 Advanced PDF document understanding including page layout, reading order & table structures
- 🧩 Unified, expressive DoclingDocument representation format
- 🤖 Plug-and-play integrations incl. LangChain, LlamaIndex, Crew AI & Haystack for agentic AI
- 🔍 OCR support for scanned PDFs
- 💻 Simple and convenient CLI
Explore the documentation to discover plenty examples and unlock the full power of Docling!
Coming soon
- ♾️ Equation & code extraction
- 📝 Metadata extraction, including title, authors, references & language
Installation
To use Docling, simply install docling
from your package manager, e.g. pip:
pip install docling
Works on macOS, Linux and Windows environments. Both x86_64 and arm64 architectures.
More detailed installation instructions are available in the docs.
Getting started
To convert individual documents, use convert()
, for example:
from docling.document_converter import DocumentConverter
source = "https://arxiv.org/pdf/2408.09869" # document per local path or URL
converter = DocumentConverter()
result = converter.convert(source)
print(result.document.export_to_markdown()) # output: "## Docling Technical Report[...]"
More advanced usage options are available in the docs.
Documentation
Check out Docling's documentation, for details on installation, usage, concepts, recipes, extensions, and more.
Examples
Go hands-on with our examples, demonstrating how to address different application use cases with Docling.
Integrations
To further accelerate your AI application development, check out Docling's native integrations with popular frameworks and tools.
Apify Actor
You can run Docling in the cloud without installation using the Docling Actor on Apify platform. Simply provide a document URL and get the processed result:
apify call vancura/docling -i '{
"documentUrl": "https://arxiv.org/pdf/2408.09869",
"outputFormat": "markdown",
"ocr": true
}'
The Actor stores results in:
- Processed document in key-value store (
OUTPUT_RESULT
) - Processing logs (
DOCLING_LOG
) - Dataset record with result URL and status
Read more about the Docling Actor, including how to use it via the Apify API and CLI.
Get help and support
Please feel free to connect with us using the discussion section.
Technical report
For more details on Docling's inner workings, check out the Docling Technical Report.
Contributing
Please read Contributing to Docling for details.
References
If you use Docling in your projects, please consider citing the following:
@techreport{Docling,
author = {Deep Search Team},
month = {8},
title = {Docling Technical Report},
url = {https://arxiv.org/abs/2408.09869},
eprint = {2408.09869},
doi = {10.48550/arXiv.2408.09869},
version = {1.0.0},
year = {2024}
}
License
The Docling codebase is under MIT license. For individual model usage, please refer to the model licenses found in the original packages.
IBM ❤️ Open Source AI
Docling has been brought to you by IBM.