How to Deploy Your Own Self-Hosted AI Chatbot with Thunderbolt

Introduction

Thunderbolt, an open-source AI client from Mozilla’s for-profit arm MZLA Technologies, empowers enterprises to run self-hosted chatbots on their own infrastructure. This guide walks you through deploying Thunderbolt for your organisation, ensuring data sovereignty and custom AI control. Follow these steps to set up your sovereign AI client.

What You Need

• A server or virtual machine with at least 8GB RAM, 4 CPU cores, and 50GB storage (Linux recommended).
• Docker and Docker Compose installed.
• Git for cloning the repository.
• An LLM model (e.g., Llama 2, Mistral) hosted locally or accessed via API.
• Network configuration (ports 80/443 open for HTTPS, or internal access).
• Administrative access to install packages and configure firewall.

Step-by-Step Guide

Step 1: Understand Thunderbolt’s Purpose

Thunderbolt is a “sovereign AI client” designed for organisations that need self-hosted chatbots. It integrates with open-source LLMs, keeping all data on-premises. Ensure your team agrees on use cases (e.g., internal FAQ, customer support) before deployment.

Step 2: Prepare Your Environment

Install Docker and Docker Compose on your server. For Ubuntu, run:

sudo apt update && sudo apt install docker.io docker-compose -y
sudo systemctl enable docker && sudo systemctl start docker

Verify with docker --version. Ensure Git is installed (sudo apt install git -y).

Step 3: Download Thunderbolt

Clone the official Thunderbolt repository from Mozilla’s MZLA Technologies GitHub:

git clone https://github.com/mzla-technologies/thunderbolt.git
cd thunderbolt

This will fetch the latest open-source code. Check the README for version-specific instructions.

Step 4: Configure Thunderbolt

Edit the .env file (copy from .env.example) to set essential variables:

• LLM_ENDPOINT: URL of your LLM service (e.g., http://localhost:11434 for Ollama).
• API_KEY: If your LLM requires authentication.
• DATABASE_URL: For persistent chat logs (e.g., PostgreSQL).
• SECRET_KEY: A random string for session security.

Step 5: Launch Thunderbolt

Use Docker Compose to start the services:

docker-compose up -d

This will pull the necessary images and start the web interface on port 8080 (default). Verify with docker ps to ensure all containers are running (e.g., thunderbolt-web, thunderbolt-api).

Step 6: Connect an LLM

Thunderbolt supports multiple LLM sources. If using Ollama, run docker run -d -p 11434:11434 ollama/ollama and pull a model (ollama pull llama2). Then update your .env with the endpoint. Restart Thunderbolt containers: docker-compose restart.

Step 7: Access and Test the Chatbot

Open your browser to http://your-server-ip:8080. You should see Thunderbolt’s interface. Send a test message – the response should come from your self-hosted LLM. If not, check logs (docker-compose logs) for errors.

Step 8: Secure and Scale

Enable HTTPS using a reverse proxy (e.g., Nginx with Let’s Encrypt). Set up user authentication via OAuth2 or LDAP for enterprise use. For higher load, increase replicas in docker-compose.yml (under thunderbolt-web: replicas: 3).

Tips for Success

• Name clarity: Avoid confusion with Intel’s Thunderbolt trademark – rename your deployment or use internal branding.
• Model selection: Start with a small model like Mistral 7B for testing, then scale to larger ones for production.
• Data privacy: Ensure your LLM endpoint does not send data to external services – verify network policies.
• Monitoring: Use Docker logs and metrics (Prometheus) to track usage and performance.
• Updates: Regularly pull the latest code from the repository and rebuild containers with docker-compose pull && docker-compose up -d.

By following this guide, you’ll have a fully functional, self-hosted AI chatbot powered by Thunderbolt – giving your enterprise sovereign control over AI interactions.