LokiChat AI

An offline AI chat application that runs entirely on your local machine, providing privacy and control over your conversations.

Overview

LokiChat AI is a comprehensive chat application that combines:

• Backend: Spring Boot with Spring AI framework
• AI Model: Ollama for local AI model execution
• Frontend: Next.js for modern chat interface
• Database: PostgreSQL for conversation persistence
• Deployment: Docker Compose for seamless local deployment

Features

• 🤖 Offline AI chat with local model execution
• 💬 Create and manage multiple conversations
• 💾 Persistent chat history stored in PostgreSQL
• 🐳 Docker containerization for easy deployment
• 🌐 Modern web interface built with Next.js
• 🔒 Complete privacy - no data leaves your machine

Prerequisites

• Docker and Docker Compose
• Node.js (for frontend development)
• PM2 (for process management)
• Apache (for production frontend serving)

Quick Start with Docker Compose

1. Environment Configuration

First, create a .env file in your project root:

DB_USER=your_db_username
DB_PASSWORD=your_secure_password

⚠️ Important: Change the default database credentials in your .env file for security.

2. Docker Compose Setup

Create or update your docker-compose.yml:

3. Deploy with Docker Compose

# Start all services
docker-compose up -d

# View logs
docker-compose logs -f

4. Configure AI Models

After starting the containers, you need to pull your preferred AI model into Ollama:

# Access the Ollama container
docker exec -it lokichat_ollama ollama pull llama2

# Or pull other models like:
docker exec -it lokichat_ollama ollama pull mistral
docker exec -it lokichat_ollama ollama pull codellama

# List available models
docker exec -it lokichat_ollama ollama list

Popular model options:

• llama2 - General purpose chat
• mistral - Fast and efficient
• codellama - Code-focused conversations
• phi - Lightweight option

Frontend Setup (Next.js)

Development Mode

# Navigate to frontend directory
cd frontend

# Install dependencies
npm install

# Run development server
npm run dev

Production Deployment with PM2

1. Install PM2 globally

npm install -g pm2

2. Build the Next.js application

# In frontend directory
npm run build

3. Create PM2 ecosystem file

Create ecosystem.config.js in your frontend directory:

module.exports = {
  apps: [{
    name: 'lokichat-frontend',
    script: 'npm',
    args: 'start',
    cwd: '/path/to/your/frontend',
    instances: 1,
    autorestart: true,
    watch: false,
    max_memory_restart: '1G',
    env: {
      NODE_ENV: 'production',
      PORT: 3000
    }
  }]
};

4. Start with PM2

# Start the application
pm2 start ecosystem.config.js

# Save PM2 configuration
pm2 save

# Setup PM2 to start on boot
pm2 startup

Backend Setup (Spring Boot)

Local Development

# Navigate to backend directory
cd backend

# Run with Maven
./mvnw spring-boot:run

# Or with Gradle
./gradlew bootRun

Configuration

Update application.yml or application.properties:

spring:
  datasource:
    url: jdbc:postgresql://localhost:5432/lokichat
    username: ${DB_USER:lokichat}
    password: ${DB_PASSWORD:password}
    driver-class-name: org.postgresql.Driver
  
  jpa:
    hibernate:
      ddl-auto: update
    show-sql: false
    properties:
      hibernate:
        dialect: org.hibernate.dialect.PostgreSQLDialect

  ai:
    ollama:
      base-url: http://localhost:11434
      chat:
        options:
          model: llama2
          temperature: 0.7

Complete Deployment Guide

Option 1: Docker Compose (Recommended)

# Clone the repository
git clone <your-repo-url>
cd LokiChat-AI

# Configure environment
cp .env.example .env
# Edit .env with your database credentials

# Start all services
docker-compose up -d

# Pull AI model
docker exec -it lokichat_ollama ollama pull llama2

# Your application is now running at:
# - Backend: http://localhost:8081
# - Frontend: http://localhost:3000 (if running separately)
# - Ollama: http://localhost:11434

Option 2: Separate Deployment

1. Database: Start PostgreSQL
2. Backend: Run Spring Boot application
3. Ollama: Start Ollama service and pull models
4. Frontend: Build and deploy with PM2 + Apache

Troubleshooting

Common Issues

1. Database Connection Issues

   # Check if PostgreSQL is running
   docker-compose ps
   
   # View database logs
   docker-compose logs lokichat-db

2. AI Model Not Responding

   # Check Ollama status
   docker exec -it lokichat_ollama ollama list
   
   # Test Ollama directly
   curl http://localhost:11434/api/generate -d '{
     "model": "llama2",
     "prompt": "Hello world"
   }'

3. Frontend Not Loading

   # Check PM2 status
   pm2 list
   
   # View frontend logs
   pm2 logs lokichat-frontend

Security Considerations

• Change default database credentials in .env
• Use environment variables for sensitive configuration
• Consider using HTTPS in production
• Regularly update Docker images
• Monitor resource usage

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

Support

For issues and questions, please create an issue in the GitHub repository.