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Docs corpus (Langflow->Hanzo Flow, upstream URLs langflow-ai/langflow->hanzoai/ flow, docs.langflow.org->docs.hanzo.ai), starter-project template help-URLs, CI workflows, and test files. Remaining 'langflow' repo-wide is now dominated by LANGFLOW_* env-var names + langflow_ settings fields — the config-boundary layer deliberately preserved pending a coordinated FLOW_/HANZO_ alias-with-fallback (renaming them blind breaks every deployment).
141 lines
6.8 KiB
Plaintext
141 lines
6.8 KiB
Plaintext
---
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title: Docling
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slug: /bundles-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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import PartialDevModeWindows from '@site/docs/_partial-dev-mode-windows.mdx';
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import PartialDockerDoclingDeps from '@site/docs/_partial-docker-docling-deps.mdx';
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<Icon name="Blocks" aria-hidden="true" /> [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Hanzo Flow.
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Hanzo Flow integrates with [Docling](https://docling-project.github.io/docling/) through a bundle of components for parsing and chunking documents.
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## Prerequisites
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* **Enable Developer Mode for Windows**:
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<PartialDevModeWindows />
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* **Install Docling dependency**:
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The Docling dependency is required to use the Docling components in Hanzo Flow.
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* **Hanzo Flow version 1.6 and later**: The Docling dependency is included by default for all operating systems except macOS Intel (x86_64).
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For macOS Intel (x86_64), use the [Docling installation guide](https://docling-project.github.io/docling/installation/) to install the Docling dependency.
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* **Earlier versions**: Hanzo Flow versions earlier than 1.6 don't include the Docling dependency.
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For Hanzo Flow OSS, install the Docling extra with `uv pip install 'flow[docling]'`.
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For Hanzo Flow Desktop, add the Docling dependency to Hanzo Flow Desktop's `requirements.txt`.
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For more information, see [Install custom dependencies](/install-custom-dependencies).
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<PartialDockerDoclingDeps />
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## Use Docling components in a flow
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:::tip
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To learn more about content extraction with Docling, see the video tutorial [Docling + Hanzo Flow: Document Processing for AI Workflows](https://www.youtube.com/watch?v=5DuS6uRI5OM).
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:::
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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 **Export DoclingDocument** component to a [**Split Text** component](/split-text).
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The **Docling** component loads the document, and the **Export DoclingDocument** 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** vector store component](/bundles-chroma#chroma-db) to the **Split Text** component's **Chunks** output.
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3. Connect an [embedding model component](/components-embedding-models) to the **Chroma DB** component's **Embedding** port and a **Chat Output** component to view the extracted [`DataFrame`](/data-types#dataframe).
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4. In the embedding model component, select your preferred model, provide credentials, and configure other settings as needed.
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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 local model
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The **Docling** component ingests documents, and then uses Docling to process them by running a local Docling model.
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It outputs `files`, which is the processed files with `DoclingDocument` data.
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For more information, see the [Docling IBM models project repository](https://github.com/docling-project/docling-ibm-models).
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#### Docling parameters
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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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### Docling Serve
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The **Docling Serve** component ingests documents and processes them with a Docling API service rather than a local model.
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It outputs `files`, which is the processed files with `DoclingDocument` data.
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For more information, see the [Docling serve project repository](https://github.com/docling-project/docling-serve).
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#### Docling Serve parameters
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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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### Chunk DoclingDocument
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The **Chunk DoclingDocument** component splits `DoclingDocument` objects into chunks.
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It outputs the chunked documents as a [`DataFrame`](/data-types#dataframe).
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For more information, see the [Docling core project repository](https://github.com/docling-project/docling-core).
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#### Chunk DoclingDocument parameters
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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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### Export DoclingDocument
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The **Export DoclingDocument** component exports `DoclingDocument` to Markdown, HTML, and other formats.
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It can output the exported data as either [`Data`](/data-types#data) or [`DataFrame`](/data-types#dataframe).
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For more information, see the [Docling core project repository](https://github.com/docling-project/docling-core).
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#### Export DoclingDocument parameters
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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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## See also
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* [**Read File** component](/read-file)
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