lightrag/docs/DockerDeployment.md

LightRAG

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

🚀 Installation

Prerequisites

• Python 3.10+
• Git
• Docker (optional for Docker deployment)

Native Installation

1. Clone the repository:

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

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

2. 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

3. Create and activate virtual environment:

# Linux/MacOS
python -m venv venv
source venv/bin/activate

# Windows PowerShell
python -m venv venv
.\venv\Scripts\Activate

4. Install dependencies:

# Both platforms
pip install -r requirements.txt

🐳 Docker Deployment

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

1. Build and start the container:

docker-compose up -d

Configuration Options

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
• MAX_EMBED_TOKENS: Maximum embedding token size

Security

• LIGHTRAG_API_KEY: API key for authentication

Data Storage Paths

The system uses the following paths for data storage:

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

Example Deployments

1. Using with Ollama:

LLM_BINDING=ollama
LLM_BINDING_HOST=http://localhost:11434
LLM_MODEL=mistral
EMBEDDING_BINDING=ollama
EMBEDDING_BINDING_HOST=http://localhost:11434
EMBEDDING_MODEL=bge-m3

2. Using with OpenAI:

LLM_BINDING=openai
LLM_MODEL=gpt-3.5-turbo
EMBEDDING_BINDING=openai
EMBEDDING_MODEL=text-embedding-ada-002
OPENAI_API_KEY=your-api-key

API Usage

Once deployed, you can interact with the API at http://localhost:9621

Example query using PowerShell:

$headers = @{
    "X-API-Key" = "your-api-key"
    "Content-Type" = "application/json"
}
$body = @{
    query = "your question here"
} | ConvertTo-Json

Invoke-RestMethod -Uri "http://localhost:9621/query" -Method Post -Headers $headers -Body $body

Example query using curl:

curl -X POST "http://localhost:9621/query" \
     -H "X-API-Key: your-api-key" \
     -H "Content-Type: application/json" \
     -d '{"query": "your question here"}'

🔒 Security

Remember to:

1. Set a strong API key in production
2. Use SSL in production environments
3. Configure proper network security

📦 Updates

To update the Docker container:

docker-compose pull
docker-compose up -d --build

To update native installation:

# Linux/MacOS
git pull
source venv/bin/activate
pip install -r requirements.txt

# Windows PowerShell
git pull
.\venv\Scripts\Activate
pip install -r requirements.txt