> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/lumina-ai-inc/chunkr/llms.txt
> Use this file to discover all available pages before exploring further.

# Introduction

> Open-source document intelligence API for production-ready layout analysis, OCR, and semantic chunking

# Welcome to Chunkr

Chunkr is a production-ready service for document layout analysis, OCR, and semantic chunking. Convert PDFs, PPTs, Word docs, and images into RAG/LLM-ready chunks with structured output.

<CardGroup cols={2}>
  <Card title="Quickstart" icon="rocket" href="/quickstart">
    Get up and running with your first API request in minutes
  </Card>

  <Card title="Installation" icon="docker" href="/installation">
    Set up Chunkr locally with Docker and GPU/CPU support
  </Card>

  <Card title="API Reference" icon="code" href="/api/overview">
    Explore the complete API documentation
  </Card>

  <Card title="GitHub" icon="github" href="https://github.com/lumina-ai-inc/chunkr">
    View the open-source repository and contribute
  </Card>
</CardGroup>

## Key Features

<AccordionGroup>
  <Accordion title="Layout Analysis" icon="grid">
    Automatically detect and segment layout elements including:

    * Tables with structure preservation
    * Images and figures
    * Headers and sections
    * Lists and captions
    * Formulas and equations
    * Text paragraphs
  </Accordion>

  <Accordion title="OCR + Bounding Boxes" icon="text">
    Optical Character Recognition with precise bounding box coordinates:

    * Configurable OCR strategies (`All` or `Auto`)
    * High-resolution image processing
    * Text layer extraction from native PDFs
    * Support for scanned documents
  </Accordion>

  <Accordion title="Structured Output" icon="file-code">
    Get your documents in multiple formats:

    * HTML with semantic markup
    * Markdown for documentation
    * JSON with coordinates and metadata
    * Configurable per segment type
  </Accordion>

  <Accordion title="Vision-Language Model Processing" icon="brain">
    LLM-powered content enhancement:

    * Table structure extraction
    * Image description generation
    * Content summarization
    * Semantic chunking for RAG
  </Accordion>
</AccordionGroup>

## Open Source vs Cloud API

The open-source version uses community models and is perfect for development and testing. For production workloads with higher accuracy and enterprise reliability, check out the [Chunkr Cloud API](https://www.chunkr.ai).

| Feature             | Open Source           | Cloud API                   |
| ------------------- | --------------------- | --------------------------- |
| **Layout Analysis** | Open-source models    | Proprietary in-house models |
| **OCR Accuracy**    | Community OCR engines | Optimized OCR stack         |
| **VLM Processing**  | Basic open VLMs       | Enhanced proprietary VLMs   |
| **Excel Support**   | ❌                     | ✅ Native parser             |
| **Infrastructure**  | Self-hosted           | Fully managed cloud         |
| **Support**         | Discord community     | Dedicated support           |

<Info>
  The open-source release uses the AGPL-3.0 license. For commercial use without AGPL compliance, contact [mehul@chunkr.ai](mailto:mehul@chunkr.ai).
</Info>

## Document Types Supported

Chunkr processes a wide range of document formats:

* **PDF** - Native and scanned PDFs with full layout analysis
* **PowerPoint** - PPT and PPTX presentations
* **Word** - DOC and DOCX documents
* **Images** - PNG, JPG, TIFF, and other image formats

<Note>
  Excel support is available exclusively in the [Cloud API](https://www.chunkr.ai).
</Note>

## Architecture Overview

Chunkr is built with a modern microservices architecture:

* **Server** - FastAPI-based REST API (Rust/Actix-Web)
* **Task Queue** - Redis-backed job processing
* **Segmentation** - YOLO-based layout detection with GPU acceleration
* **OCR** - DocTR (Document Text Recognition) engine
* **Storage** - MinIO for object storage, PostgreSQL for metadata
* **Web UI** - React-based interface for testing and visualization

All services are containerized and orchestrated with Docker Compose for easy deployment.

## Use Cases

<CardGroup cols={2}>
  <Card title="RAG Pipelines" icon="robot">
    Extract and chunk documents for retrieval-augmented generation systems
  </Card>

  <Card title="Document Processing" icon="file-lines">
    Automate extraction of structured data from unstructured documents
  </Card>

  <Card title="Content Migration" icon="arrows-turn-right">
    Convert legacy documents to modern formats (HTML, Markdown)
  </Card>

  <Card title="Search Indexing" icon="magnifying-glass">
    Extract text and metadata for full-text search engines
  </Card>
</CardGroup>

## Community & Support

Join our community and get help:

* **Discord** - [Join the community](https://discord.gg/XzKWFByKzW)
* **GitHub Issues** - [Report bugs](https://github.com/lumina-ai-inc/chunkr/issues)
* **Email** - [mehul@chunkr.ai](mailto:mehul@chunkr.ai)
* **Schedule a call** - [Book 30 minutes](https://cal.com/mehulc/30min)

## Next Steps

<Steps>
  <Step title="Quick Start">
    Follow our [quickstart guide](/quickstart) to make your first API request
  </Step>

  <Step title="Installation">
    Set up Chunkr locally with our [installation guide](/installation)
  </Step>

  <Step title="Explore the API">
    Dive into the [API reference](/api/overview) to learn about all available features
  </Step>
</Steps>