Added Docker container setup

.env.example

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+# Server Configuration
+HOST=0.0.0.0
+PORT=9621
+
+# Directory Configuration
+WORKING_DIR=/app/data/rag_storage
+INPUT_DIR=/app/data/inputs
+
+# LLM Configuration
+LLM_BINDING=ollama
+LLM_BINDING_HOST=http://localhost:11434
+LLM_MODEL=mistral-nemo:latest
+
+# Embedding Configuration
+EMBEDDING_BINDING=ollama
+EMBEDDING_BINDING_HOST=http://localhost:11434
+EMBEDDING_MODEL=bge-m3:latest
+
+# RAG Configuration
+MAX_ASYNC=4
+MAX_TOKENS=32768
+EMBEDDING_DIM=1024
+MAX_EMBED_TOKENS=8192
+
+# Security (empty for no key)
+LIGHTRAG_API_KEY=your-secure-api-key-here
+
+# Logging
+LOG_LEVEL=INFO
+
+# Optional SSL Configuration
+#SSL=true
+#SSL_CERTFILE=/path/to/cert.pem
+#SSL_KEYFILE=/path/to/key.pem
+
+# Optional Timeout
+#TIMEOUT=30

Dockerfile

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+# Build stage
+FROM python:3.11-slim as builder
+
+WORKDIR /app
+
+# Install build dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy only requirements files first to leverage Docker cache
+COPY requirements.txt .
+COPY lightrag/api/requirements.txt ./lightrag/api/
+
+# Install dependencies
+RUN pip install --user --no-cache-dir -r requirements.txt
+RUN pip install --user --no-cache-dir -r lightrag/api/requirements.txt
+
+# Final stage
+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Copy only necessary files from builder
+COPY --from=builder /root/.local /root/.local
+COPY . .
+
+# Make sure scripts in .local are usable
+ENV PATH=/root/.local/bin:$PATH
+
+# Create necessary directories
+RUN mkdir -p /app/data/rag_storage /app/data/inputs
+
+# Expose the default port
+EXPOSE 9621
+
+# Set entrypoint
+ENTRYPOINT ["python", "-m", "lightrag.api.lightrag_server"]

docker-compose.yml

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+version: '3.8'
+
+services:
+  lightrag:
+    build: .
+    ports:
+      - "${PORT:-9621}:9621"
+    volumes:
+      - ./data/rag_storage:/app/data/rag_storage
+      - ./data/inputs:/app/data/inputs
+    env_file:
+      - .env
+    environment:
+      - TZ=UTC
+    restart: unless-stopped
+    networks:
+      - lightrag_net
+
+networks:
+  lightrag_net:
+    driver: bridge

docs/DockerDeployment.md

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+# 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
+```