base on Vision infrastructure to turn complex documents into RAG/LLM-ready data <br />
<div align="center">
<a href="https://github.com/lumina-ai-inc/chunkr">
<img src="images/logo.svg" alt="Chunkr Logo" width="80" height="80">
</a>
<h3 align="center">Chunkr | Open Source Document Intelligence API</h3>
<p align="center">
Production-ready service for document layout analysis, OCR, and semantic chunking.<br />
Convert PDFs, PPTs, Word docs & images into RAG/LLM-ready chunks.
<br /><br />
<b>Layout Analysis</b> | <b>OCR + Bounding Boxes</b> | <b>Structured HTML & Markdown</b> | <b>Vision-Language Model Processing</b>
<br /><br />
👉 <b>Note:</b> The <a href="https://github.com/lumina-ai-inc/chunkr">open-source AGPL version</a> is **different** from our fully managed <a href="https://www.chunkr.ai">Cloud API</a>.
The open-source release uses community/open-source models, while the Cloud API runs **proprietary in-house models** for higher accuracy, speed, and enterprise reliability.
<br /><br />
<a href="https://www.chunkr.ai"><img src="https://img.shields.io/badge/Try_it_out-chunkr.ai-blue?style=flat&logo=rocket&height=20" alt="Try it out" height="20"></a>
<a href="https://github.com/lumina-ai-inc/chunkr/issues/new"><img src="https://img.shields.io/badge/Report_Bug-GitHub_Issues-red?style=flat&logo=github&height=20" alt="Report Bug" height="20"></a>
<a href="#connect-with-us"><img src="https://img.shields.io/badge/Contact-Get_in_Touch-green?style=flat&logo=mail&height=20" alt="Contact" height="20"></a>
<a href="https://discord.gg/XzKWFByKzW"><img src="https://img.shields.io/badge/Discord-Join_Community-5865F2?style=flat&logo=discord&logoColor=white&height=20" alt="Discord" height="20"></a>
<a href="https://deepwiki.com/lumina-ai-inc/chunkr"><img src="https://deepwiki.com/badge.svg" alt="Ask DeepWiki"></a>
</p>
</div>
<div align="center">
<a href="https://www.chunkr.ai" width="1200" height="630">
<img src="https://chunkr.ai/og-image.png" alt="Chunkr Cloud API">
</a>
</div>
## Table of Contents
- [Table of Contents](#table-of-contents)
- [(Super) Quick Start](#super-quick-start)
- [Documentation](#documentation)
- [Open Source vs Cloud API vs Enterprise](#open-source-vs-cloud-api-vs-enterprise)
- [Quick Start with Docker Compose](#quick-start-with-docker-compose)
- [LLM Configuration](#llm-configuration)
- [Using models.yaml (Recommended)](#using-modelsyaml-recommended)
- [Using environment variables (Basic)](#using-environment-variables-basic)
- [Common LLM API Providers](#common-llm-api-providers)
- [Licensing](#licensing)
- [Connect With Us](#connect-with-us)
## Open Source vs Cloud API vs Enterprise
| Feature | Open Source Repo (good) | Cloud API - chunkr.ai (best) | Enterprise |
|---------|--------------------|------------------------|------------|
| **Perfect for** | Development & testing | Production workloads | Large-scale / High-security |
| **Layout Analysis** | Uses open-source models | Proprietary in-house models | In-house + custom-tuned |
| **OCR Accuracy** | Community OCR engines | Optimized OCR stack | Optimized + domain-tuned |
| **VLM Processing** | Basic open VLMs | Enhanced proprietary VLMs | Custom fine-tunes |
| **Excel Support** | ❌ | ✅ Native parser | ✅ Native parser |
| **Document Types** | PDF, PPT, Word, Images | PDF, PPT, Word, Images, Excel | PDF, PPT, Word, Images, Excel |
| **Infrastructure** | Self-hosted | Fully managed cloud | Managed / On-prem |
| **Support** | Discord community | Dedicated support | Dedicated founding team |
| **Migration Support** | Community-driven | Docs + email | Dedicated migration team |
---
The **open-source release** is ideal if you want transparency, local hosting, or to experiment with Chunkr’s pipeline.
For **best performance, production reliability, and access to in-house models**, we recommend the <a href="https://www.chunkr.ai">Chunkr Cloud API</a>.
For **high-security or regulated industries**, our **Enterprise edition** offers on-prem or VPC deployments.
## Quick Start with Docker Compose
1. Prerequisites:
- [Docker and Docker Compose](https://docs.docker.com/get-docker/)
- [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) (for GPU support, optional)
2. Clone the repo:
```bash
git clone https://github.com/lumina-ai-inc/chunkr
cd chunkr
```
3. Set up environment variables:
```bash
# Copy the example environment file
cp .env.example .env
# Configure your llm models
cp models.example.yaml models.yaml
```
For more information on how to set up LLMs, see [here](#llm-configuration).
4. Start the services:
```bash
# For GPU deployment:
docker compose up -d
# For CPU-only deployment:
docker compose -f compose.yaml -f compose.cpu.yaml up -d
# For Mac ARM architecture (M1, M2, M3, etc.):
docker compose -f compose.yaml -f compose.cpu.yaml -f compose.mac.yaml up -d
```
5. Access the services:
- Web UI: `http://localhost:5173`
- API: `http://localhost:8000`
6. Stop the services when done:
```bash
# For GPU deployment:
docker compose down
# For CPU-only deployment:
docker compose -f compose.yaml -f compose.cpu.yaml down
# For Mac ARM architecture (M1, M2, M3, etc.):
docker compose -f compose.yaml -f compose.cpu.yaml -f compose.mac.yaml down
```
## LLM Configuration
Chunkr supports two ways to configure LLMs:
1. **models.yaml file**: Advanced configuration for multiple LLMs with additional options
2. **Environment variables**: Simple configuration for a single LLM
### Using models.yaml (Recommended)
For more flexible configuration with multiple models, default/fallback options, and rate limits:
1. Copy the example file to create your configuration:
```bash
cp models.example.yaml models.yaml
```
2. Edit the models.yaml file with your configuration. Example:
```yaml
models:
- id: gpt-4o
model: gpt-4o
provider_url: https://api.openai.com/v1/chat/completions
api_key: "your_openai_api_key_here"
default: true
rate-limit: 200 # requests per minute - optional
```
Benefits of using models.yaml:
- Configure multiple LLM providers simultaneously
- Set default and fallback models
- Add distributed rate limits per model
- Reference models by ID in API requests (see docs for more info)
>Read the `models.example.yaml` file for more information on the available options.
### Using environment variables (Basic)
You can use any OpenAI API compatible endpoint by setting the following variables in your .env file:
```
LLM__KEY:
LLM__MODEL:
LLM__URL:
```
### Common LLM API Providers
Below is a table of common LLM providers and their configuration details to get you started:
| Provider | API URL | Documentation |
| ---------------- | ------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------- |
| OpenAI | https://api.openai.com/v1/chat/completions | [OpenAI Docs](https://platform.openai.com/docs) |
| Google AI Studio | https://generativelanguage.googleapis.com/v1beta/openai/chat/completions | [Google AI Docs](https://ai.google.dev/gemini-api/docs/openai) |
| OpenRouter | https://openrouter.ai/api/v1/chat/completions | [OpenRouter Models](https://openrouter.ai/models) |
| Self-Hosted | http://localhost:8000/v1 | [VLLM](https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html) or [Ollama](https://ollama.com/blog/openai-compatibility) |
## Licensing
The core of this project is dual-licensed:
1. [GNU Affero General Public License v3.0 (AGPL-3.0)](LICENSE)
2. Commercial License
To use Chunkr without complying with the AGPL-3.0 license terms you can [contact us](mailto:
[email protected]) or visit our [website](https://chunkr.ai).
## Connect With Us
- 📧 Email: [
[email protected]](mailto:
[email protected])
- 📅 Schedule a call: [Book a 30-minute meeting](https://cal.com/mehulc/30min)
- 🌐 Visit our website: [chunkr.ai](https://chunkr.ai)
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