# 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:
```bash
# Linux/MacOS
git clone https://github.com/ParisNeo/LightRAG.git
cd LightRAG
```
```powershell
# Windows PowerShell
git clone https://github.com/ParisNeo/LightRAG.git
cd LightRAG
```

2. Configure your environment:
```bash
# Linux/MacOS
cp .env.example .env
# Edit .env with your preferred configuration
```
```powershell
# Windows PowerShell
Copy-Item .env.example .env
# Edit .env with your preferred configuration
```

3. Create and activate virtual environment:
```bash
# Linux/MacOS
python -m venv venv
source venv/bin/activate
```
```powershell
# Windows PowerShell
python -m venv venv
.\venv\Scripts\Activate
```

4. Install dependencies:
```bash
# 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:
```bash
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:
```env
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:
```env
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:
```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:
```bash
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:
```bash
docker-compose pull
docker-compose up -d --build
```

To update native installation:
```bash
# Linux/MacOS
git pull
source venv/bin/activate
pip install -r requirements.txt
```
```powershell
# Windows PowerShell
git pull
.\venv\Scripts\Activate
pip install -r requirements.txt
```