Documentation

Installation Guide

Complete guide for installing and configuring ADAPT-AI in development and production environments.

Installation Methods

Package Installation

Quick installation via pip for immediate use

pip install adapt-ai[full]

Development Setup

Clone repository for development and contributions

git clone https://github.com/perfecxion-ai/adapt-ai.git

Development Installation

1Clone Repository

git clone https://github.com/perfecxion-ai/adapt-ai.git
cd adapt-ai

2Create Virtual Environment

# Create virtual environment
python -m venv venv

# Activate virtual environment
# Linux/macOS:
source venv/bin/activate

# Windows:
venv\Scripts\activate

3Install Dependencies

# Install all dependencies including development tools
pip install -r requirements-dev.txt

# Or install specific components:
pip install -r requirements.txt       # Core dependencies only
pip install -r requirements-ml.txt    # Machine learning components
pip install -r requirements-test.txt  # Testing dependencies

4Database Setup

Start PostgreSQL and Redis services using Docker:

# Start database services
make docker-up

# Or manually with docker-compose:
docker-compose up -d postgres redis

# Wait for services to be ready
sleep 30

# Initialize database schema
alembic upgrade head

5Configure Environment

# Copy environment template
cp .env.template .env

# Edit configuration (optional - defaults work for development)
nano .env

The default configuration works out of the box for development. You only need to modify the .env file for production deployment or custom configurations.

Production Installation

Docker Deployment

# Pull production image
docker pull perfecxion/adapt-ai:latest

# Run with environment variables
docker run -d \
  --name adapt-ai \
  -p 8000:8000 \
  -e ADAPT_API_KEY="your-production-key" \
  -e DATABASE_URL="postgresql://..." \
  -e REDIS_URL="redis://..." \
  perfecxion/adapt-ai:latest

Kubernetes Deployment

# Apply Kubernetes manifests
kubectl apply -f k8s/namespace.yaml
kubectl apply -f k8s/configmap.yaml
kubectl apply -f k8s/secrets.yaml
kubectl apply -f k8s/deployment.yaml
kubectl apply -f k8s/service.yaml
kubectl apply -f k8s/ingress.yaml

# Verify deployment
kubectl get pods -n adapt-ai
kubectl get svc -n adapt-ai

Configuration Reference

Environment Variables

# API Configuration
ADAPT_API_KEY=your-api-key-here
API_GATEWAY_PORT=8000
DISCOVERY_SERVICE_PORT=8001

# Database Configuration
DATABASE_URL=postgresql+asyncpg://postgres:password@localhost:5432/adapt_db
REDIS_URL=redis://localhost:6379

# Security Configuration
JWT_SECRET_KEY=your-secret-key-change-in-production
JWT_EXPIRATION_HOURS=24
ENABLE_CORS=true
CORS_ORIGINS=["http://localhost:3000"]

# ML System Configuration
ML_MODEL_PATH=./models
ML_TRAINING_DATA_PATH=./data/training
ML_ENABLE_GPU=false
ML_BATCH_SIZE=32

# Logging Configuration
LOG_LEVEL=INFO
LOG_FORMAT=json
ENABLE_METRICS=true

# Feature Flags
ENABLE_DISCOVERY=true
ENABLE_TESTING=true
ENABLE_ML_SYSTEM=true
ENABLE_WEBSOCKET=true

# Performance Configuration
MAX_CONCURRENT_WORKERS=10
REQUEST_TIMEOUT_SECONDS=300
CACHE_TTL_SECONDS=3600

System Requirements

Minimum Requirements

Python 3.11 or higher
8GB RAM
5GB disk space
PostgreSQL 14+
Redis 6+

Recommended for Production

16GB+ RAM
4+ CPU cores
50GB+ SSD storage
NVIDIA GPU (for ML features)
Load balancer for HA

Verify Installation

After installation, verify everything is working correctly:

# Check ADAPT-AI version
python -c "import adapt_ai; print(adapt_ai.__version__)"

# Test API connection
adapt-ai test-connection

# Run health checks
curl http://localhost:8000/health
curl http://localhost:8001/health

# Run self-test suite
adapt-ai self-test --verbose

# Check service status
adapt-ai status