Quick Start

Overview

This guide will help you quickly set up and run Youtu-GraphRAG using either Docker or Web UI deployment. Choose the method that best fits your environment and needs.

Prerequisites

Method 1: Docker Deployment (Recommended)

Docker provides the most reliable and consistent deployment experience across different environments.

Step 1: Install Docker

If you don't have Docker installed, follow the official Docker installation guide.

Step 2: Clone and Configure

# 1. Clone Youtu-GraphRAG project
git clone https://github.com/TencentCloudADP/youtu-graphrag
cd youtu-graphrag

# 2. Create .env file from template
cp .env.example .env

Step 3: Configure Your LLM API

Edit the .env file with your LLM API credentials:

# Example configuration for DeepSeek API
LLM_MODEL=deepseek-chat
LLM_BASE_URL=https://api.deepseek.com
LLM_API_KEY=sk-xxxxxx

# Example configuration for OpenAI API
# LLM_MODEL=gpt-4
# LLM_BASE_URL=https://api.openai.com/v1
# LLM_API_KEY=sk-xxxxxx

Step 4: Build and Run

# 3. Build Docker image
docker build -t youtu_graphrag:v1 .

# 4. Run the container
docker run -d -p 8000:8000 youtu_graphrag:v1

# 5. Verify the service is running
curl -v http://localhost:8000

Step 5: Access the Web Interface

Open your browser and navigate to: http://localhost:8000

Method 2: Web UI Experience

This method requires Python 3.10+ and is suitable for development and testing environments.

Step 1: System Requirements

Ensure you have Python 3.10 or higher installed:

python --version
# Should output Python 3.10.x or higher

Step 2: Clone Repository

# 1. Clone the project
git clone https://github.com/TencentCloudADP/youtu-graphrag
cd youtu-graphrag

Step 3: Create Virtual Environment

It's recommended to create a virtual environment to avoid dependency conflicts:

# 2. Create virtual environment in project directory
python -m venv youtu-graphrag-env

# 3. Activate virtual environment
# On macOS/Linux:
source youtu-graphrag-env/bin/activate

# On Windows:
# youtu-graphrag-env\Scripts\activate

Step 4: Configure Environment

# 4. Create .env file
cp .env.example .env

Edit the .env file with your LLM API settings (same as Docker method above).

Step 5: Setup and Launch

# 5. Setup environment (installs dependencies in virtual environment)
./setup_env.sh

# 6. Launch the web service
./start.sh

Step 5: Access the Application

The service will be available at: http://localhost:8000

Getting Help

If you encounter issues:

1. Check the logs:

   # For Docker deployment
   docker logs <container_id>
   
   # For Web UI deployment
   tail -f output/logs/app.log

2. Seek help from the community:

   Share your error details and logs with the community:

   • GitHub Issues
   • Chat with Source Code
   • Community Discord

What's Next?

After successfully setting up Youtu-GraphRAG:

• Explore the Demo: Try the built-in demo datasets
• Upload Your Data: Start building knowledge graphs with your own documents
• Customize Schemas: Adapt the system to your specific domain
• Advanced Features: Explore community detection and agentic retrieval capabilities

🎉 Congratulations! You now have Youtu-GraphRAG up and running. Start exploring the power of Graph Retrieval-Augmented Complex Reasoning!