base on Get your documents ready for gen AI <p align="center"> <a href="https://github.com/docling-project/docling"> <img loading="lazy" alt="Docling" src="https://github.com/docling-project/docling/raw/main/docs/assets/docling_processing.png" width="100%"/> </a> </p> # Docling <p align="center"> <a href="https://trendshift.io/repositories/12132" target="_blank"><img src="https://trendshift.io/api/badge/repositories/12132" alt="DS4SD%2Fdocling | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a> </p> [](https://arxiv.org/abs/2408.09869) [](https://docling-project.github.io/docling/) [](https://pypi.org/project/docling/) [](https://pypi.org/project/docling/) [](https://github.com/astral-sh/uv) [](https://github.com/astral-sh/ruff) [](https://pydantic.dev) [](https://github.com/pre-commit/pre-commit) [](https://opensource.org/licenses/MIT) [](https://pepy.tech/projects/docling) [](https://apify.com/vancura/docling) [](https://app.dosu.dev/097760a8-135e-4789-8234-90c8837d7f1c/ask?utm_source=github) [](https://docling.ai/discord) [](https://www.bestpractices.dev/projects/10101) [](https://lfaidata.foundation/projects/) Docling simplifies document processing, parsing diverse formats — including advanced PDF understanding — and providing seamless integrations with the gen AI ecosystem. ## Features * 🗂️ Parsing of [multiple document formats][supported_formats] incl. PDF, DOCX, PPTX, XLSX, HTML, WAV, MP3, VTT, images (PNG, TIFF, JPEG, ...), and more * 📑 Advanced PDF understanding incl. page layout, reading order, table structure, code, formulas, image classification, and more * 🧬 Unified, expressive [DoclingDocument][docling_document] representation format * ↪️ Various [export formats][supported_formats] and options, including Markdown, HTML, [DocTags](https://arxiv.org/abs/2503.11576) and lossless JSON * 🔒 Local execution capabilities for sensitive data and air-gapped environments * 🤖 Plug-and-play [integrations][integrations] incl. LangChain, LlamaIndex, Crew AI & Haystack for agentic AI * 🔍 Extensive OCR support for scanned PDFs and images * 👓 Support of several Visual Language Models ([GraniteDocling](https://huggingface.co/ibm-granite/granite-docling-258M)) * 🎙️ Audio support with Automatic Speech Recognition (ASR) models * 🔌 Connect to any agent using the [MCP server](https://docling-project.github.io/docling/usage/mcp/) * 💻 Simple and convenient CLI ### What's new * 📤 Structured [information extraction][extraction] \[🧪 beta\] * 📑 New layout model (**Heron**) by default, for faster PDF parsing * 🔌 [MCP server](https://docling-project.github.io/docling/usage/mcp/) for agentic applications * 💬 Parsing of Web Video Text Tracks (WebVTT) files ### Coming soon * 📝 Metadata extraction, including title, authors, references & language * 📝 Chart understanding (Barchart, Piechart, LinePlot, etc) * 📝 Complex chemistry understanding (Molecular structures) ## Installation To use Docling, simply install `docling` from your package manager, e.g. pip: ```bash pip install docling ``` Works on macOS, Linux and Windows environments. Both x86_64 and arm64 architectures. More [detailed installation instructions](https://docling-project.github.io/docling/installation/) are available in the docs. ## Getting started To convert individual documents with python, use `convert()`, for example: ```python 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](https://docling-project.github.io/docling/usage/advanced_options/) are available in the docs. ## CLI Docling has a built-in CLI to run conversions. ```bash docling https://arxiv.org/pdf/2206.01062 ``` You can also use 🥚[GraniteDocling](https://huggingface.co/ibm-granite/granite-docling-258M) and other VLMs via Docling CLI: ```bash docling --pipeline vlm --vlm-model granite_docling https://arxiv.org/pdf/2206.01062 ``` This will use MLX acceleration on supported Apple Silicon hardware. Read more [here](https://docling-project.github.io/docling/usage/) ## Documentation Check out Docling's [documentation](https://docling-project.github.io/docling/), for details on installation, usage, concepts, recipes, extensions, and more. ## Examples Go hands-on with our [examples](https://docling-project.github.io/docling/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](https://docling-project.github.io/docling/integrations/) with popular frameworks and tools. ## Get help and support Please feel free to connect with us using the [discussion section](https://github.com/docling-project/docling/discussions). ## Technical report For more details on Docling's inner workings, check out the [Docling Technical Report](https://arxiv.org/abs/2408.09869). ## Contributing Please read [Contributing to Docling](https://github.com/docling-project/docling/blob/main/CONTRIBUTING.md) for details. ## References If you use Docling in your projects, please consider citing the following: ```bib @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. ## LF AI & Data Docling is hosted as a project in the [LF AI & Data Foundation](https://lfaidata.foundation/projects/). ### IBM ❤️ Open Source AI The project was started by the AI for knowledge team at IBM Research Zurich. [supported_formats]: https://docling-project.github.io/docling/usage/supported_formats/ [docling_document]: https://docling-project.github.io/docling/concepts/docling_document/ [integrations]: https://docling-project.github.io/docling/integrations/ [extraction]: https://docling-project.github.io/docling/examples/extraction/ ", Assign "at most 3 tags" to the expected json: {"id":"12132","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"