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update github pages
Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>
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README.md
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README.md
@ -11,7 +11,7 @@
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</p>
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[](https://arxiv.org/abs/2408.09869)
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[](https://ds4sd.github.io/docling/)
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[](https://docling-project.github.io/docling/)
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[](https://pypi.org/project/docling/)
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[](https://pypi.org/project/docling/)
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[](https://python-poetry.org/)
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@ -51,7 +51,7 @@ pip install docling
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Works on macOS, Linux and Windows environments. Both x86_64 and arm64 architectures.
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More [detailed installation instructions](https://ds4sd.github.io/docling/installation/) are available in the docs.
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More [detailed installation instructions](https://docling-project.github.io/docling/installation/) are available in the docs.
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## Getting started
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@ -66,23 +66,23 @@ result = converter.convert(source)
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print(result.document.export_to_markdown()) # output: "## Docling Technical Report[...]"
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```
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More [advanced usage options](https://ds4sd.github.io/docling/usage/) are available in
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More [advanced usage options](https://docling-project.github.io/docling/usage/) are available in
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the docs.
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## Documentation
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Check out Docling's [documentation](https://ds4sd.github.io/docling/), for details on
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Check out Docling's [documentation](https://docling-project.github.io/docling/), for details on
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installation, usage, concepts, recipes, extensions, and more.
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## Examples
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Go hands-on with our [examples](https://ds4sd.github.io/docling/examples/),
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Go hands-on with our [examples](https://docling-project.github.io/docling/examples/),
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demonstrating how to address different application use cases with Docling.
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## Integrations
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To further accelerate your AI application development, check out Docling's native
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[integrations](https://ds4sd.github.io/docling/integrations/) with popular frameworks
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[integrations](https://docling-project.github.io/docling/integrations/) with popular frameworks
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and tools.
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## Get help and support
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@ -123,6 +123,6 @@ For individual model usage, please refer to the model licenses found in the orig
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Docling has been brought to you by IBM.
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[supported_formats]: https://ds4sd.github.io/docling/usage/supported_formats/
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[docling_document]: https://ds4sd.github.io/docling/concepts/docling_document/
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[integrations]: https://ds4sd.github.io/docling/integrations/
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[supported_formats]: https://docling-project.github.io/docling/usage/supported_formats/
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[docling_document]: https://docling-project.github.io/docling/concepts/docling_document/
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[integrations]: https://docling-project.github.io/docling/integrations/
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@ -121,7 +121,7 @@ def download(
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"Using the CLI:",
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f"`docling --artifacts-path={output_dir} FILE`",
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"\n",
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"Using Python: see the documentation at <https://ds4sd.github.io/docling/usage>.",
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"Using Python: see the documentation at <https://docling-project.github.io/docling/usage>.",
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)
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"ocrmac is not correctly installed. "
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"Please install it via `pip install ocrmac` to use this OCR engine. "
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"Alternatively, Docling has support for other OCR engines. See the documentation: "
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"https://ds4sd.github.io/docling/installation/"
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"https://docling-project.github.io/docling/installation/"
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)
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try:
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from ocrmac import ocrmac
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"Note that tesserocr might have to be manually compiled for working with "
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"your Tesseract installation. The Docling documentation provides examples for it. "
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"Alternatively, Docling has support for other OCR engines. See the documentation: "
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"https://ds4sd.github.io/docling/installation/"
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"https://docling-project.github.io/docling/installation/"
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)
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missing_langs_errmsg = (
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"tesserocr is not correctly configured. No language models have been detected. "
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"Please ensure that the TESSDATA_PREFIX envvar points to tesseract languages dir. "
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"You can find more information how to setup other OCR engines in Docling "
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"documentation: "
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"https://ds4sd.github.io/docling/installation/"
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"https://docling-project.github.io/docling/installation/"
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)
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try:
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This is an example of using [Docling](https://ds4sd.github.io/docling/) for converting structured data (XML) into a unified document\n",
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"This is an example of using [Docling](https://docling-project.github.io/docling/) for converting structured data (XML) into a unified document\n",
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"representation format, `DoclingDocument`, and leverage its riched structured content for RAG applications.\n",
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"\n",
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"Data used in this example consist of patents from the [United States Patent and Trademark Office (USPTO)](https://www.uspto.gov/) and medical\n",
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"> 👉 **NOTE**: As you see above, using the `HybridChunker` can sometimes lead to a warning from the transformers library, however this is a \"false alarm\" — for details check [here](https://ds4sd.github.io/docling/faq/#hybridchunker-triggers-warning-token-indices-sequence-length-is-longer-than-the-specified-maximum-sequence-length-for-this-model)."
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"> 👉 **NOTE**: As you see above, using the `HybridChunker` can sometimes lead to a warning from the transformers library, however this is a \"false alarm\" — for details check [here](https://docling-project.github.io/docling/faq/#hybridchunker-triggers-warning-token-indices-sequence-length-is-longer-than-the-specified-maximum-sequence-length-for-this-model)."
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]
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},
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{
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"## A recipe 🧑🍳 🐥 💚\n",
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"\n",
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"This notebook demonstrates how to build a Retrieval-Augmented Generation (RAG) system using:\n",
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"- [Docling](https://ds4sd.github.io/docling/) for document parsing and chunking\n",
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"- [Docling](https://docling-project.github.io/docling/) for document parsing and chunking\n",
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"- [Azure AI Search](https://azure.microsoft.com/products/ai-services/ai-search/?msockid=0109678bea39665431e37323ebff6723) for vector indexing and retrieval\n",
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"- [Azure OpenAI](https://azure.microsoft.com/products/ai-services/openai-service?msockid=0109678bea39665431e37323ebff6723) for embeddings and chat completion\n",
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"\n",
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"\n",
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"## A recipe 🧑🍳 🐥 💚\n",
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"\n",
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"This is a code recipe that uses [Weaviate](https://weaviate.io/) to perform RAG over PDF documents parsed by [Docling](https://ds4sd.github.io/docling/).\n",
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"This is a code recipe that uses [Weaviate](https://weaviate.io/) to perform RAG over PDF documents parsed by [Docling](https://docling-project.github.io/docling/).\n",
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"\n",
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"In this notebook, we accomplish the following:\n",
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"* Parse the top machine learning papers on [arXiv](https://arxiv.org/) using Docling\n",
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site_name: Docling
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site_url: https://ds4sd.github.io/docling/
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site_url: https://docling-project.github.io/docling/
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repo_name: docling-project/docling
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repo_url: https://github.com/docling-project/docling
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<html lang="en">
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<head>
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<link rel="icon" type="image/png"
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href="https://ds4sd.github.io/docling/assets/logo.png"/>
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href="https://docling-project.github.io/docling/assets/logo.png"/>
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<meta charset="UTF-8">
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<title>
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Powered by Docling
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