lightrag/docs/DockerDeployment.md

LightRAG Docker Deployment

A lightweight Knowledge Graph Retrieval-Augmented Generation system with multiple LLM backend support.

🚀 Preparation

Clone the repository:

# Linux/MacOS
git clone https://github.com/HKUDS/LightRAG.git
cd LightRAG

# Windows PowerShell
git clone https://github.com/HKUDS/LightRAG.git
cd LightRAG

Configure your environment:

# Linux/MacOS
cp .env.example .env
# Edit .env with your preferred configuration

# Windows PowerShell
Copy-Item .env.example .env
# Edit .env with your preferred configuration

LightRAG can be configured using environment variables in the .env file:

Server Configuration

• HOST: Server host (default: 0.0.0.0)
• PORT: Server port (default: 9621)

LLM Configuration

• LLM_BINDING: LLM backend to use (lollms/ollama/openai)
• LLM_BINDING_HOST: LLM server host URL
• LLM_MODEL: Model name to use

Embedding Configuration

• EMBEDDING_BINDING: Embedding backend (lollms/ollama/openai)
• EMBEDDING_BINDING_HOST: Embedding server host URL
• EMBEDDING_MODEL: Embedding model name

RAG Configuration

• MAX_ASYNC: Maximum async operations
• MAX_TOKENS: Maximum token size
• EMBEDDING_DIM: Embedding dimensions

🐳 Docker Deployment

Docker instructions work the same on all platforms with Docker Desktop installed.

Build Optimization

The Dockerfile uses BuildKit cache mounts to significantly improve build performance:

• Automatic cache management: BuildKit is automatically enabled via # syntax=docker/dockerfile:1 directive
• Faster rebuilds: Only downloads changed dependencies when uv.lock or bun.lock files are modified
• Efficient package caching: UV and Bun package downloads are cached across builds
• No manual configuration needed: Works out of the box in Docker Compose and GitHub Actions

Start LightRAG server:

docker compose up -d

LightRAG Server uses the following paths for data storage:

data/
├── rag_storage/    # RAG data persistence
└── inputs/         # Input documents

Updates

To update the Docker container:

docker compose pull
docker compose down
docker compose up

Offline deployment

Software packages requiring transformers, torch, or cuda will is not preinstalled in the dokcer images. Consequently, document extraction tools such as Docling, as well as local LLM models like Hugging Face and LMDeploy, can not be used in an off line enviroment. These high-compute-resource-demanding services should not be integrated into LightRAG. Docling will be decoupled and deployed as a standalone service.

📦 Build Docker Images

For local development and testing

# Build and run with Docker Compose (BuildKit automatically enabled)
docker compose up --build

# Or explicitly enable BuildKit if needed
DOCKER_BUILDKIT=1 docker compose up --build

Note: BuildKit is automatically enabled by the # syntax=docker/dockerfile:1 directive in the Dockerfile, ensuring optimal caching performance.

For production release

multi-architecture build and push:

# Use the provided build script
./docker-build-push.sh

The build script will:

• Check Docker registry login status
• Create/use buildx builder automatically
• Build for both AMD64 and ARM64 architectures
• Push to GitHub Container Registry (ghcr.io)
• Verify the multi-architecture manifest

Prerequisites:

Before building multi-architecture images, ensure you have:

• Docker 20.10+ with Buildx support
• Sufficient disk space (20GB+ recommended for offline image)
• Registry access credentials (if pushing images)