docs: document new integrations (#532)
Signed-off-by: Panos Vagenas <35837085+vagenas@users.noreply.github.com>
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Docling is available as an extraction backend in the [Bee][github] framework.
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- 💻 [Bee GitHub][github]
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- 📖 [Bee Docs][docs]
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- 📖 [Bee docs][docs]
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- 📦 [Bee NPM][package]
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[github]: https://github.com/i-am-bee
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docs/integrations/cloudera.md
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docs/integrations/cloudera.md
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Docling is available in [Cloudera](https://www.cloudera.com/) through the *RAG Studio*
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Accelerator for Machine Learning Projects (AMP).
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- 💻 [RAG Studio AMP GitHub][github]
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[github]: https://github.com/cloudera/CML_AMP_RAG_Studio
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## Get started
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Docling is used by the [Data Prep Kit](https://ibm.github.io/data-prep-kit/) open-source toolkit for preparing unstructured data for LLM application development ranging from laptop scale to datacenter scale.
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Below you find the Data Prep Kit modules powered by Docling.
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## PDF ingestion to Parquet
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## Components
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### PDF ingestion to Parquet
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- 💻 [PDF-to-Parquet GitHub](https://github.com/IBM/data-prep-kit/tree/dev/transforms/language/pdf2parquet)
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- 📖 [PDF-to-Parquet Docs](https://ibm.github.io/data-prep-kit/transforms/language/pdf2parquet/python/)
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- 📖 [PDF-to-Parquet docs](https://ibm.github.io/data-prep-kit/transforms/language/pdf2parquet/python/)
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## Document chunking
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### Document chunking
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- 💻 [Doc Chunking GitHub](https://github.com/IBM/data-prep-kit/tree/dev/transforms/language/doc_chunk)
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- 📖 [Doc Chunking Docs](https://ibm.github.io/data-prep-kit/transforms/language/doc_chunk/python/)
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- 📖 [Doc Chunking docs](https://ibm.github.io/data-prep-kit/transforms/language/doc_chunk/python/)
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Docling is available as a file conversion method in [DocETL](https://github.com/ucbepic/docetl):
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- 💻 [DocETL GitHub][github]
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- 📖 [DocETL Docs][docs]
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- 📖 [DocETL docs][docs]
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- 📦 [DocETL PyPI][pypi]
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[github]: https://github.com/ucbepic/docetl
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Docling is powering document processing in [InstructLab](https://instructlab.ai/),
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Docling is powering document processing in [InstructLab][home],
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enabling users to unlock the knowledge hidden in documents and present it to
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InstructLab's fine-tuning for aligning AI models to the user's specific data.
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More details can be found in this [blog post][blog].
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- 🏠 [InstructLab Home][home]
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- 🏠 [InstructLab home][home]
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- 💻 [InstructLab GitHub][github]
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- 🧑🏻💻 [InstructLab UI][ui]
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- 📖 [InstructLab Docs][docs]
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<!-- - 📝 [Blog post]() -->
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- 📖 [InstructLab docs][docs]
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[home]: https://instructlab.ai
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[github]: https://github.com/instructlab
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Docling is available in [Kotaemon](https://cinnamon.github.io/kotaemon/) as the `DoclingReader` loader:
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- 💻 [Kotaemon GitHub][github]
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- 📖 [DoclingReader Docs][docs]
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- ⚙️ [Docling Setup in Kotaemon][setup]
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- 📖 [DoclingReader docs][docs]
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- ⚙️ [Docling setup in Kotaemon][setup]
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[github]: https://github.com/Cinnamon/kotaemon
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[docs]: https://cinnamon.github.io/kotaemon/reference/loaders/docling_loader/
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## Get started
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Docling is available as an official [LlamaIndex](https://docs.llamaindex.ai/) extension.
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To get started, check out the [step-by-step guide in LlamaIndex](https://docs.llamaindex.ai/en/stable/examples/data_connectors/DoclingReaderDemo/).
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Reads document files and uses Docling to populate LlamaIndex `Document` objects — either serializing Docling's data model (losslessly, e.g. as JSON) or exporting to a simplified format (lossily, e.g. as Markdown).
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- 💻 [Docling Reader GitHub](https://github.com/run-llama/llama_index/tree/main/llama-index-integrations/readers/llama-index-readers-docling)
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- 📖 [Docling Reader Docs](https://docs.llamaindex.ai/en/stable/api_reference/readers/docling/)
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- 📖 [Docling Reader docs](https://docs.llamaindex.ai/en/stable/api_reference/readers/docling/)
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- 📦 [Docling Reader PyPI](https://pypi.org/project/llama-index-readers-docling/)
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### Docling Node Parser
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Reads LlamaIndex `Document` objects populated in Docling's format by Docling Reader and, using its knowledge of the Docling format, parses them to LlamaIndex `Node` objects for downstream usage in LlamaIndex applications, e.g. as chunks for embedding.
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- 💻 [Docling Node Parser GitHub](https://github.com/run-llama/llama_index/tree/main/llama-index-integrations/node_parser/llama-index-node-parser-docling)
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- 📖 [Docling Node Parser Docs](https://docs.llamaindex.ai/en/stable/api_reference/node_parser/docling/)
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- 📖 [Docling Node Parser docs](https://docs.llamaindex.ai/en/stable/api_reference/node_parser/docling/)
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- 📦 [Docling Node Parser PyPI](https://pypi.org/project/llama-index-node-parser-docling/)
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Docling is available in [Prodigy][home] as a [Prodigy-PDF plugin][plugin] recipe.
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- 🌐 [Prodigy Home][home]
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- 🔌 [Prodigy-PDF Plugin][plugin]
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- 🧑🏽🍳 [pdf-spans.manual Recipe][recipe]
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More details can be found in this [blog post][blog].
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- 🌐 [Prodigy home][home]
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- 🔌 [Prodigy-PDF plugin][plugin]
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- 🧑🏽🍳 [pdf-spans.manual recipe][recipe]
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[home]: https://prodi.gy/
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[plugin]: https://prodi.gy/docs/plugins#pdf
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[recipe]: https://prodi.gy/docs/plugins#pdf-spans.manual
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[blog]: https://explosion.ai/blog/pdfs-nlp-structured-data
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docs/integrations/rhel_ai.md
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docs/integrations/rhel_ai.md
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Docling is powering document processing in [Red Hat Enterprise Linux AI][home] (RHEL AI),
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enabling users to unlock the knowledge hidden in documents and present it to
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InstructLab's fine-tuning for aligning AI models to the user's specific data.
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More details can be found in this [blog post][blog].
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- 🏠 [RHEL AI home][home]
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[home]: https://www.redhat.com/en/technologies/linux-platforms/enterprise-linux/ai
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[blog]: https://www.redhat.com/en/blog/docling-missing-document-processing-companion-generative-ai
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# spaCy
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Docling is available in [spaCy](https://spacy.io/) as the *spaCy Layout* plugin.
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Docling is available in [spaCy](https://spacy.io/) as the "SpaCy Layout" plugin:
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More details can be found in this [blog post][blog].
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- 💻 [SpacyLayout GitHub][github]
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- 📖 [SpacyLayout Docs][docs]
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- 📖 [SpacyLayout docs][docs]
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- 📦 [SpacyLayout PyPI][pypi]
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[github]: https://github.com/explosion/spacy-layout
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[docs]: https://github.com/explosion/spacy-layout?tab=readme-ov-file#readme
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[pypi]: https://pypi.org/project/spacy-layout/
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[blog]: https://explosion.ai/blog/pdfs-nlp-structured-data
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docs/integrations/txtai.md
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docs/integrations/txtai.md
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Docling is available as a text extraction backend for [txtai](https://neuml.github.io/txtai/).
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- 💻 [txtai GitHub][github]
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- 📖 [txtai docs][docs]
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- 📖 [txtai Docling backend][integration_docs]
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[github]: https://github.com/neuml/txtai
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[docs]: https://neuml.github.io/txtai
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[integration_docs]: https://neuml.github.io/txtai/pipeline/data/filetohtml/#docling
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@ -87,13 +87,16 @@ nav:
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- Integrations:
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- Integrations: integrations/index.md
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- "🐝 Bee": integrations/bee.md
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- "Cloudera": integrations/cloudera.md
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- "Data Prep Kit": integrations/data_prep_kit.md
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- "DocETL": integrations/docetl.md
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- "🐶 InstructLab": integrations/instructlab.md
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- "Kotaemon": integrations/kotaemon.md
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- "🦙 LlamaIndex": integrations/llamaindex.md
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- "Prodigy": integrations/prodigy.md
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- "Red Hat Enterprise Linux AI": integrations/rhel_ai.md
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- "spaCy": integrations/spacy.md
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- "txtai": integrations/txtai.md
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# - "LangChain 🦜🔗": integrations/langchain.md
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- API reference:
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- Document Converter: api_reference/document_converter.md
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