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RAG & Knowledge Systems

Docling Studio

Docling Studio is an open-source, browser-based application for configuring Docling document extraction, inspecting PDF results, and preparing extracted content for retrieval systems. It supports OCR and table extraction, displays text and structural elements against PDF pages, and can chunk and embed content for OpenSearch indexing, with optional Neo4j graph storage.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Docling Studio — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when a document pipeline needs a visual review stage between PDF extraction and indexing, or when preparing document content for search and knowledge-graph workflows.

Who it is for

  • Teams preparing PDF collections for retrieval-augmented generation or vector search.
  • Document-processing practitioners who need to inspect extraction output and its page-level location.
  • Developers integrating Docling processing with OpenSearch or Neo4j.

Documented uses

  • Configure OCR, table extraction and other supported Docling processing options.
  • Review extracted text, tables, images, formulas and bounding boxes in context on PDF pages.
  • Split extracted content into editable semantic chunks with configurable token limits.
  • Prepare chunks for embedding and OpenSearch vector indexing.
  • Store document structure and chunk relationships in Neo4j.
Before you choose

Requirements & limits

The project is publicly available on GitHub under the MIT license. The README documents Docker deployment and local development; no hosted-service pricing or account requirement is stated.

  • The local Docker image runs Docling in-process and is approximately 1.9 GB; the remote image is approximately 270 MB and requires a Docling Serve endpoint.
  • The README recommends 6 GB of memory for the local image and 2 GB for remote mode; it states that a GPU is not required.
  • OpenSearch ingestion is an opt-in deployment profile, distinct from the basic backend and frontend setup.
  • The app uses Vue 3 and TypeScript for its frontend and FastAPI, Docling, SQLite and pdf2image in its backend; the README specifies Python 3.12+ and Node 20+ for local development.
  • Upload size and page-count limits are configurable; the documented default maximum file size is 50 MB and the page-count limit defaults to unlimited.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Start Docling Studio with Docker

Launch the local browser interface and begin a PDF analysis.

Before you start

  • Docker
  • A machine with enough memory for the chosen image; the README recommends 6 GB for local mode and 2 GB for remote mode.

Steps

  1. Run the documented local-image Docker command, publishing port 3000.

  2. Open the local address at port 3000 in a browser.

  3. Upload a PDF and configure its extraction pipeline in the Studio.

Expected outcome

A browser-based PDF analysis with extracted content and visual results.

Guide limitations

  • The local image runs Docling in-process on CPU and is approximately 1.9 GB.
  • The first analysis downloads roughly 400 MB of Docling machine-learning models and may take longer.
  • The guide covers document analysis, not the optional OpenSearch or Neo4j ingestion setup.
Documentation 1 ↗
Source-based practical guide

Run the remote conversion image

Launch the lightweight Studio image configured to use an existing Docling Serve instance.

Before you start

  • Docker
  • An accessible Docling Serve instance and its URL.

Steps

  1. Run the remote image while setting DOCLING_SERVE_URL to the Docling Serve endpoint.

  2. Open the local Studio address at port 3000 in a browser.

  3. Upload a PDF and configure extraction through the Studio.

Expected outcome

The browser interface delegates document conversion to the configured Docling Serve instance.

Guide limitations

  • The external Docling Serve instance is a prerequisite and is not installed by this command.
  • The README documents an optional API-key environment variable for Docling Serve, but this recipe does not configure one.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Vector search

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Docling StudioConfigure OCR, table extraction and other supported Docling processing options.Self-hosted, WebThe project is publicly available on GitHub under the MIT license. The README documents Docker deployment and local development; no hosted-service pricing or account requirement is stated.
Chatbot with PDF for Semantic SearchPrepare document content as embeddings and save it to a supported vector database.WebThe repository is identified as open source and publicly visible on GitHub.
AI LLM PlaygroundAsk questions about PDF documents through an assistant interface.Web
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
12 October 2026
Original website title
scub-france/docling-Studio · Official homepage title · retained in its source language
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