> ## Documentation Index
> Fetch the complete documentation index at: https://langchain.idochub.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Docling

> [Docling](https://github.com/DS4SD/docling) parses PDF, DOCX, PPTX, HTML, and other formats into a rich unified representation including document layout, tables etc., making them ready for generative AI workflows like RAG.
>
> This integration provides Docling's capabilities via the `DoclingLoader` document loader.

## Installation and Setup

Simply install `langchain-docling` from your package manager, e.g. pip:

<CodeGroup>
  ```bash pip theme={null}
  pip install langchain-docling
  ```

  ```bash uv theme={null}
  uv add langchain-docling
  ```
</CodeGroup>

## Document Loader

The `DoclingLoader` class in `langchain-docling` seamlessly integrates Docling into
LangChain, enabling you to:

* use various document types in your LLM applications with ease and speed, and
* leverage Docling's rich representation for advanced, document-native grounding.

Basic usage looks as follows:

```python theme={null}
from langchain_docling import DoclingLoader

FILE_PATH = ["https://arxiv.org/pdf/2408.09869"]  # Docling Technical Report

loader = DoclingLoader(file_path=FILE_PATH)

docs = loader.load()
```

For end-to-end usage check out
[this example](/oss/python/integrations/document_loaders/docling).

## Additional Resources

* [LangChain Docling integration GitHub](https://github.com/DS4SD/docling-langchain)
* [LangChain Docling integration PyPI package](https://pypi.org/project/langchain-docling/)
* [Docling GitHub](https://github.com/DS4SD/docling)
* [Docling docs](https://ds4sd.github.io/docling/)