Self-Hosted AI That's Actually Useful

Introduction

This tutorial will guide you through building a private, local, and self-hosted AI stack to enhance your daily tasks. By following these steps, you'll be able to create a secure AI environment that respects your privacy while providing useful functionality.

Step 1: Setting Up the Foundation

Begin by preparing your local environment to host the AI stack.

• Choose Your Hardware: Ensure you have a capable machine (e.g., a server or a powerful PC).
• Install Docker: This will help you manage the AI applications easily.
  • Use the following command for installation on a Linux system:

    sudo apt-get install docker.io
    

• Verify Docker Installation: Run this command to check if Docker is running:

  docker --version
  

Step 2: Deploying Ollama LLMs and APIs

Ollama provides useful language model APIs that you can run locally.

• Clone the Ollama Repository:

  git clone https://github.com/ollama/ollama.git
  

• Build the Ollama Image:

  cd ollama
  docker build -t ollama .
  

• Launch the Ollama API:

  docker run -p 8080:80 ollama
  

• Test the API: Access it through your browser at http://localhost:8080.

Step 3: Setting Up Open WebUI

Open WebUI allows you to interact with your AI models through a web interface.

• Download Open WebUI:

  git clone https://github.com/open-webui/open-webui.git
  

• Navigate to the directory and run it:

  cd open-webui
  docker-compose up
  

• Access the WebUI: Open your browser and go to http://localhost:5000.

Step 4: Implementing SearXNG for Web Search

SearXNG helps you perform private web searches without tracking.

• Install SearXNG:

  git clone https://github.com/searxng/searxng.git
  

• Configure and Run:
  • Edit the configuration file for your needs.
  • Use Docker Compose to start it:

    docker-compose up
    

• Access SearXNG: Visit http://localhost:8888.

Step 5: Setting Up Stable Diffusion with Comfy UI

For image generation, integrate Stable Diffusion through Comfy UI.

• Clone Comfy UI Repository:

  git clone https://github.com/comfyanonymous/ComfyUI.git
  

• Run the Comfy UI:

  cd ComfyUI
  docker-compose up
  

• Access the UI: Go to http://localhost:8180.

Step 6: Using Ollama for Coding Assistance

Leverage Ollama for coding help similar to a local code copilot.

• Integrate Ollama in your Development Environment:
  • Set up your IDE to utilize the Ollama API for code suggestions.
• Example API Call:

  curl -X POST http://localhost:8080/code -d '{"code": "function example() {", "context": "javascript"}'
  

Step 7: Implementing Whisper for Transcription

Whisper can convert spoken words into text.

• Install Whisper:

  git clone https://github.com/openai/whisper.git
  

• Run Whisper:

  cd whisper
  docker-compose up
  

• Test Transcription: Use an audio file to test the transcription capabilities.

Step 8: Integrating with Home Assistant

Use Assist to enhance your home automation.

• Install Home Assistant:

  docker run -d --name home-assistant -e "TZ=America/New_York" -v /PATH_TO_YOUR_CONFIG:/config -p 8123:8123 homeassistant/home-assistant
  

• Access Home Assistant: Visit http://localhost:8123 to configure your home automation.

Conclusion

You've now set up a comprehensive local AI stack that can assist with various tasks, from coding to home automation. Each component can be customized further to suit your needs. Explore the capabilities of these AI tools to enhance your productivity and secure your privacy. For further learning, consider experimenting with additional models or integrating more services into your stack.