Chunkr | Open Source Document Intelligence API

Production-ready 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 & Markdown | Vision-Language Model Processing

👉 Note: The open-source AGPL version is **different** from our fully managed Cloud API. 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.

## 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 Chunkr Cloud API. 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:mehul@chunkr.ai) or visit our [website](https://chunkr.ai). ## Connect With Us - 📧 Email: [mehul@chunkr.ai](mailto:mehul@chunkr.ai) - 📅 Schedule a call: [Book a 30-minute meeting](https://cal.com/mehulc/30min) - 🌐 Visit our website: [chunkr.ai](https://chunkr.ai)