open-webui-docs/docs/features/rag/document-extraction/docling.md

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4000	Docling Document Extraction

:::warning

This tutorial is a community contribution and is not supported by the Open WebUI team. It serves only as a demonstration on how to customize Open WebUI for your specific use case. Want to contribute? Check out the contributing tutorial.

:::

🐤 Docling Document Extraction

This documentation provides a step-by-step guide to integrating Docling with Open WebUI. Docling is a document processing library designed to transform a wide range of file formats—including PDFs, Word documents, spreadsheets, HTML, and images—into structured data such as JSON or Markdown. With built-in support for layout detection, table parsing, and language-aware processing, Docling streamlines document preparation for AI applications like search, summarization, and retrieval-augmented generation, all through a unified and extensible interface.

Prerequisites

• Open WebUI instance
• Docker installed on your system
• Docker network set up for Open WebUI

Integration Steps

Step 1: Run the Docker Command for Docling-Serve

docker run -p 5001:5001 -e DOCLING_SERVE_ENABLE_UI=true quay.io/docling-project/docling-serve

*With GPU support:

docker run --gpus all -p 5001:5001 -e DOCLING_SERVE_ENABLE_UI=true quay.io/docling-project/docling-serve-cu124

Step 2: Configure Open WebUI to use Docling

• Log in to your Open WebUI instance.
• Navigate to the Admin Panel settings menu.
• Click on Settings.
• Click on the Documents tab.
• Change the Default content extraction engine dropdown to Docling.
• Update the context extraction engine URL to http://host.docker.internal:5001.
• Save the changes.

(optional) Step 3: Configure Docling's picture description features

• on the Documents tab:
• Activate Describe Pictures in Documents button.
• Below, choose a description mode: local or API
  • local: vision model will run in the same context as Docling itself
  • API: Docling will make a call to an external service/container (i.e. Ollama)
• fill in an object value as described at https://github.com/docling-project/docling-serve/blob/main/docs/usage.md#picture-description-options
• Save the changes.

Make sure the object value is a valid JSON! Working examples below

{
  "repo_id": "HuggingFaceTB/SmolVLM-256M-Instruct",
  "generation_config": {
    "max_new_tokens": 200,
    "do_sample": false
  },
  "prompt": "Describe this image in a few sentences."
}

{
  "url": "http://localhost:11434/v1/chat/completions",
  "params": {
    "model": "qwen2.5vl:7b-q4_K_M"
  },
  "timeout": 60,
  "prompt": "Describe this image in great details. "
}

Verifying Docling in Docker

To verify that Docling is working correctly in a Docker environment, you can follow these steps:

1. Start the Docling Docker Container

First, ensure that the Docling Docker container is running. You can start it using the following command:

docker run -p 5001:5001 -e DOCLING_SERVE_ENABLE_UI=true quay.io/docling-project/docling-serve

This command starts the Docling container and maps port 5001 from the container to port 5001 on your local machine.

2. Verify the Server is Running

• Go to http://127.0.0.1:5001/ui/
• The URL should lead to a UI to use Docling

3. Verify the Integration

• You can try uploading some files via the UI and it should return output in MD format or your desired format

Docling Parameters Reference

When using Open WebUI with Docling version 2.0.0+, you can configure advanced processing options via the DOCLING_PARAMS JSON object in the Admin Settings > Documents or via the environment variable.

Common Parameters

Parameter	Type	Description	Allowed Values / Examples
pdf_backend	string	The PDF parsing engine to use.	dlparse_v1, dlparse_v2, dlparse_v4, pypdfium2
table_mode	string	Quality/speed tradeoff for tables.	fast, accurate
ocr_engine	string	The OCR library for scanned documents.	tesseract, easyocr, ocrmac, rapidocr, tesserocr
do_ocr	bool	Whether to perform OCR.	true, false
force_ocr	bool	Force OCR even on digital PDFs.	true, false
pipeline	string	Processing pipeline complexity.	standard, fast
ocr_lang	list[string]	Languages for OCR.	["eng"] (3-letter) for Tesseract; ["en"] (2-letter) for EasyOCR

:::tip Language Codes: The format of ocr_lang depends on the engine:

• Tesseract / Tesserocr: Uses 3-letter ISO 639-2 codes (e.g., eng, deu, fra, spa).
• EasyOCR / RapidOCR: Typically use 2-letter ISO 639-1 codes (e.g., en, de, fr, es). :::

:::tip dlparse vs dbparse: Note that the backend names use dlparse (Deep Learning Parse), not dbparse. For modern Docling (v2+), dlparse_v4 is generally recommended for the best balance of features. :::

Example Configuration

 {
   "do_ocr": true,
   "pdf_backend": "dlparse_v4",
   "table_mode": "accurate",
   "ocr_engine": "tesseract",
   "ocr_lang": ["eng"]
 }

Conclusion

Integrating Docling with Open WebUI is a simple and effective way to enhance document processing and content extraction capabilities. By following the steps in this guide, you can set up Docling as the default extraction engine and verify it’s working smoothly in a Docker environment. Once configured, Docling enables powerful, format-agnostic document parsing to support more advanced AI features in Open WebUI.