Chunkr | Open Source Document Intelligence API

Production-ready API service for document layout analysis, OCR, and semantic chunking.
Convert PDFs, PPTs, Word docs & images into RAG/LLM-ready chunks.

Layout Analysis | OCR + Bounding Boxes | Structured HTML and markdown | VLM Processing controls

Try it out! · Report Bug · Contact

Table of Contents

• Table of Contents
• (Super) Quick Start
• Documentation
• Self-Hosted Deployment Options
  • Quick Start with Docker Compose
  • Deployment with Kubernetes
• Licensing
• Connect With Us

(Super) Quick Start

1. Go to chunkr.ai
2. Make an account and copy your API key
3. Install our Python SDK:

   pip install chunkr-ai

4. Use the SDK to process your documents:

   from chunkr_ai import Chunkr
   
   # Initialize with your API key from chunkr.ai
   chunkr = Chunkr(api_key="your_api_key")
   
   # Upload a document (URL or local file path)
   url = "https://chunkr-web.s3.us-east-1.amazonaws.com/landing_page/input/science.pdf"
   task = chunkr.upload(url)
   
   # Export results in various formats
   task.html(output_file="output.html")
   task.markdown(output_file="output.md")
   task.content(output_file="output.txt")
   task.json(output_file="output.json")
   
   # Clean up
   chunkr.close()

Documentation

Visit our docs for more information and examples.

Self-Hosted Deployment Options

Quick Start with Docker Compose

1. Prerequisites:

   • Docker and Docker Compose
   • NVIDIA Container Toolkit (for GPU support, optional)

2. Clone the repo:

git clone https://github.com/lumina-ai-inc/chunkr
cd chunkr

3. Set up environment variables:

# Copy the example environment file
cp .env.example .env

# Configure your environment variables
# Required: LLM_KEY as your OpenAI API key

4. Start the services:

With GPU:

docker compose up -d

1. Access the services:
   • Web UI: http://localhost:5173
   • API: http://localhost:8000

Note: Requires an NVIDIA CUDA GPU

6. Stop the services when done:

docker compose down

Deployment with Kubernetes

For production environments, we provide a Helm chart and detailed deployment instructions:

1. See our detailed guide at kube/README.md
2. Includes configurations for high availability and scaling

For enterprise support and deployment assistance, contact us.

Licensing

The core of this project is dual-licensed:

1. GNU Affero General Public License v3.0 (AGPL-3.0)
2. Commercial License

To use Chunkr without complying with the AGPL-3.0 license terms you can contact us or visit our website.

Connect With Us

• 📧 Email: mehul@lumina.sh
• 📅 Schedule a call: Book a 30-minute meeting
• 🌐 Visit our website: chunkr.ai