Integrate Docling with Langflow

Langflow integrates with Docling through a suite of components for parsing documents.

Install Docling dependency

• Install the Docling extra in Langflow OSS with uv pip install langflow[docling] or uv pip install docling.

  To add a dependency to Langflow Desktop, add an entry for Docling to the application's requirements.txt file. For more information, see Install custom dependencies in Langflow Desktop.

Use Docling components in a flow

This example demonstrates how to use Docling components to split a PDF in a flow:

1. Connect a Docling and an ExportDoclingDocument component to a Split Text component. The Docling component loads the document, and the ExportDoclingDocument component converts the DoclingDocument into the format you select. This example converts the document to Markdown, with images represented as placeholders. The Split Text component will split the Markdown into chunks for the vector database to store in the next part of the flow.
2. Connect a Chroma DB component to the Split text component's Chunks output.
3. Connect an Embedding Model to Chroma's Embedding port, and a Chat Output component to view the extracted DataFrame.
4. Add your OpenAI API key to the Embedding Model.

The flow looks like this:

5. Add a file to the Docling component.
6. To run the flow, click Playground. The chunked document is loaded as vectors into your vector database.

Docling components

The following sections describe the purpose and configuration options for each component in the Docling bundle.

Docling

This component uses Docling to process input documents running the Docling models locally.

Parameters

Inputs

Name	Type	Description
files	File	The files to process.
pipeline	String	Docling pipeline to use (standard, vlm).
ocr_engine	String	OCR engine to use (easyocr, tesserocr, rapidocr, ocrmac).

Outputs

Name	Type	Description
files	File	The processed files with DoclingDocument data.

Docling Serve

This component uses Docling to process input documents connecting to your instance of Docling Serve.

Parameters

Inputs

Name	Type	Description
files	File	The files to process.
api_url	String	URL of the Docling Serve instance.
max_concurrency	Integer	Maximum number of concurrent requests for the server.
max_poll_timeout	Float	Maximum waiting time for the document conversion to complete.
api_headers	Dict	Optional dictionary of additional headers required for connecting to Docling Serve.
docling_serve_opts	Dict	Optional dictionary of additional options for Docling Serve.

Outputs

Name	Type	Description
files	File	The processed files with DoclingDocument data.

Chunk DoclingDocument

This component uses the DoclingDocument chunkers to split a document into chunks.

Parameters

Inputs

Name	Type	Description
data_inputs	Data/DataFrame	The data with documents to split in chunks.
chunker	String	Which chunker to use (HybridChunker, HierarchicalChunker).
provider	String	Which tokenizer provider (Hugging Face, OpenAI).
hf_model_name	String	Model name of the tokenizer to use with the HybridChunker when Hugging Face is chosen.
openai_model_name	String	Model name of the tokenizer to use with the HybridChunker when OpenAI is chosen.
max_tokens	Integer	Maximum number of tokens for the HybridChunker.
doc_key	String	The key to use for the DoclingDocument column.

Outputs

Name	Type	Description
dataframe	DataFrame	The chunked documents as a DataFrame.

Export DoclingDocument

This component exports DoclingDocument to Markdown, HTML, and other formats.

Parameters

Inputs

Name	Type	Description
data_inputs	Data/DataFrame	The data with documents to export.
export_format	String	Select the export format to convert the input (Markdown, HTML, Plaintext, DocTags).
image_mode	String	Specify how images are exported in the output (placeholder, embedded).
md_image_placeholder	String	Specify the image placeholder for markdown exports.
md_page_break_placeholder	String	Add this placeholder between pages in the markdown output.
doc_key	String	The key to use for the DoclingDocument column.

Outputs

Name	Type	Description
data	Data	The exported data.
dataframe	DataFrame	The exported data as a DataFrame.

Docling video tutorial

To learn more about content extraction with Docling, see the video tutorial Docling + Langflow: Document Processing for AI Workflows.