
chunkr
Vision infrastructure to turn complex documents into RAG/LLM-ready data
Stars: 2094

Chunkr is an open-source document intelligence API that provides a production-ready service for document layout analysis, OCR, and semantic chunking. It allows users to convert PDFs, PPTs, Word docs, and images into RAG/LLM-ready chunks. The API offers features such as layout analysis, OCR with bounding boxes, structured HTML and markdown output, and VLM processing controls. Users can interact with Chunkr through a Python SDK, enabling them to upload documents, process them, and export results in various formats. The tool also supports self-hosted deployment options using Docker Compose or Kubernetes, with configurations for different AI models like OpenAI, Google AI Studio, and OpenRouter. Chunkr is dual-licensed under the GNU Affero General Public License v3.0 (AGPL-3.0) and a commercial license, providing flexibility for different usage scenarios.
README:
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!
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Report Bug
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- Table of Contents
- (Super) Quick Start
- Documentation
- Self-Hosted Deployment Options
- LLM Configuration
- Licensing
- Connect With Us
- Go to chunkr.ai
- Make an account and copy your API key
- Install our Python SDK:
pip install chunkr-ai
- 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
html = task.html(output_file="output.html")
markdown = task.markdown(output_file="output.md")
content = task.content(output_file="output.txt")
task.json(output_file="output.json")
# Clean up
chunkr.close()
Visit our docs for more information and examples.
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Prerequisites:
- Docker and Docker Compose
- NVIDIA Container Toolkit (for GPU support, optional)
-
Clone the repo:
git clone https://github.com/lumina-ai-inc/chunkr
cd chunkr
- 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
For more information on how to set up LLMs, see here.
- Start the services:
With GPU:
docker compose up -d
Access the services:
- Web UI:
http://localhost:5173
- API:
http://localhost:8000
Important:
- Requires an NVIDIA CUDA GPU
- CPU-only deployment via
compose-cpu.yaml
is currently in development and not recommended for use- To use a CPU version run
docker compose -f compose-cpu.yaml up -d
- Stop the services when done:
docker compose down
For production environments, we provide a Helm chart and detailed deployment instructions:
- See our detailed guide at
kube/README.md
- Includes configurations for high availability and scaling
For enterprise support and deployment assistance, contact us.
You can use any OpenAI API compatible endpoint by setting the following variables in your .env file:
LLM__KEY:
LLM__MODEL:
LLM__URL:
LLM__KEY=your_openai_api_key
LLM__MODEL=gpt-4o
LLM__URL=https://api.openai.com/v1/chat/completions
For getting a Google AI Studio API key, see here.
LLM__KEY=your_google_ai_studio_api_key
LLM__MODEL=gemini-2.0-flash-lite
LLM__URL=https://generativelanguage.googleapis.com/v1beta/openai/chat/completions
Check here for available models.
LLM__KEY=your_open_router_api_key
LLM__MODEL=google/gemini-pro-1.5
LLM__URL=https://openrouter.ai/api/v1/chat/completions
You can use any OpenAI API compatible endpoint. To host your own LLM you can use VLLM or Ollama.
LLM__KEY=your_api_key
LLM__MODEL=model_name
LLM__URL=http://localhost:8000/v1
The core of this project is dual-licensed:
- GNU Affero General Public License v3.0 (AGPL-3.0)
- Commercial License
To use Chunkr without complying with the AGPL-3.0 license terms you can contact us or visit our website.
- 📧 Email: [email protected]
- 📅 Schedule a call: Book a 30-minute meeting
- 🌐 Visit our website: chunkr.ai
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