docs: docling integration (#8939)
* initial-content * Apply suggestions from code review * Apply suggestions from code review Co-authored-by: April I. Murphy <36110273+aimurphy@users.noreply.github.com> * peer-review * fix-broken-link * link * language --------- Co-authored-by: April I. Murphy <36110273+aimurphy@users.noreply.github.com>
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Mendon Kissling
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docs/docs/Integrations/Docling/integrations-docling.md
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docs/docs/Integrations/Docling/integrations-docling.md
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---
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title: Integrate Docling with Langflow
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slug: /integrations-docling
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---
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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import Icon from "@site/src/components/icon";
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Langflow integrates with [Docling](https://docling-project.github.io/docling/) through a suite of components for parsing documents.
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## Install Docling dependency
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* Install the Docling extra in Langflow OSS with `uv pip install langflow[docling]` or `uv pip install docling`.
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To add a dependency to Langflow Desktop, add an entry for Docling to the application's `requirements.txt` file.
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For more information, see [Install custom dependencies in Langflow Desktop](/install-custom-dependencies#langflow-desktop).
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## Use Docling components in a flow
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This example demonstrates how to use Docling components to split a PDF in a flow:
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1. Connect a **Docling** and an **ExportDoclingDocument** component to a [**Split Text**](/components-processing#split-text) component.
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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.
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The **Split Text** component will split the Markdown into chunks for the vector database to store in the next part of the flow.
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2. Connect a [**Chroma DB**](/components-vector-stores#chroma-db) component to the **Split text** component's **Chunks** output.
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3. Connect an [**Embedding Model**](/components-embedding-models) to Chroma's **Embedding** port, and a **Chat Output** component to view the extracted [DataFrame](/concepts-objects#dataframe-object).
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4. Add your OpenAI API key to the Embedding Model.
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The flow looks like this:
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5. Add a file to the **Docling** component.
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6. To run the flow, click <Icon name="Play" aria-hidden="true"/> **Playground**.
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The chunked document is loaded as vectors into your vector database.
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## Docling components
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The following sections describe the purpose and configuration options for each component in the Docling bundle.
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### Docling
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This component uses Docling to process input documents running the Docling models locally.
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<details>
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<summary>Parameters</summary>
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**Inputs**
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| Name | Type | Description |
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|------|------|-------------|
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| files | File | The files to process. |
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| pipeline | String | Docling pipeline to use (standard, vlm). |
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| ocr_engine | String | OCR engine to use (easyocr, tesserocr, rapidocr, ocrmac). |
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**Outputs**
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| Name | Type | Description |
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|------|------|-------------|
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| files | File | The processed files with DoclingDocument data. |
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</details>
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### Docling Serve
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This component uses Docling to process input documents connecting to your instance of Docling Serve.
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<details>
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<summary>Parameters</summary>
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**Inputs**
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| Name | Type | Description |
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|------|------|-------------|
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| files | File | The files to process. |
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| api_url | String | URL of the Docling Serve instance. |
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| max_concurrency | Integer | Maximum number of concurrent requests for the server. |
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| max_poll_timeout | Float | Maximum waiting time for the document conversion to complete. |
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| api_headers | Dict | Optional dictionary of additional headers required for connecting to Docling Serve. |
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| docling_serve_opts | Dict | Optional dictionary of additional options for Docling Serve. |
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**Outputs**
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| Name | Type | Description |
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|------|------|-------------|
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| files | File | The processed files with DoclingDocument data. |
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</details>
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### Chunk DoclingDocument
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This component uses the DoclingDocument chunkers to split a document into chunks.
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<details>
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<summary>Parameters</summary>
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**Inputs**
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| Name | Type | Description |
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|------|------|-------------|
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| data_inputs | Data/DataFrame | The data with documents to split in chunks. |
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| chunker | String | Which chunker to use (HybridChunker, HierarchicalChunker). |
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| provider | String | Which tokenizer provider (Hugging Face, OpenAI). |
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| hf_model_name | String | Model name of the tokenizer to use with the HybridChunker when Hugging Face is chosen. |
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| openai_model_name | String | Model name of the tokenizer to use with the HybridChunker when OpenAI is chosen. |
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| max_tokens | Integer | Maximum number of tokens for the HybridChunker. |
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| doc_key | String | The key to use for the DoclingDocument column. |
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**Outputs**
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| Name | Type | Description |
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|------|------|-------------|
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| dataframe | DataFrame | The chunked documents as a DataFrame. |
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</details>
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### Export DoclingDocument
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This component exports DoclingDocument to Markdown, HTML, and other formats.
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<details>
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<summary>Parameters</summary>
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**Inputs**
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| Name | Type | Description |
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|------|------|-------------|
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| data_inputs | Data/DataFrame | The data with documents to export. |
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| export_format | String | Select the export format to convert the input (Markdown, HTML, Plaintext, DocTags). |
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| image_mode | String | Specify how images are exported in the output (placeholder, embedded). |
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| md_image_placeholder | String | Specify the image placeholder for markdown exports. |
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| md_page_break_placeholder | String | Add this placeholder between pages in the markdown output. |
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| doc_key | String | The key to use for the DoclingDocument column. |
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**Outputs**
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| Name | Type | Description |
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|------|------|-------------|
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| data | Data | The exported data. |
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| dataframe | DataFrame | The exported data as a DataFrame. |
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</details>
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## Docling video tutorial
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To learn more about content extraction with Docling, see the video tutorial [Docling + Langflow: Document Processing for AI Workflows](https://www.youtube.com/watch?v=5DuS6uRI5OM).
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@ -293,6 +293,11 @@ module.exports = {
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id: "Integrations/Composio/integrations-composio",
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label: "Composio",
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},
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{
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type: "doc",
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id: "Integrations/Docling/integrations-docling",
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label: "Docling",
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},
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{
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type: 'category',
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label: 'Google',
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