AI Setup & Configuration

Get AI-powered log analysis working in minutes. This guide covers everything from basic API key setup to advanced configuration options for all supported AI providers.

Quick Start: Set OPENAI_API_KEY environment variable and run gonzo -f your-logs.log --ai-model="gpt-4". That's it!

Choose Your Setup Path

Select the approach that best fits your needs:

Best for: Privacy, unlimited usage, no API costs

Requirements:

8GB+ RAM (16GB+ recommended)
Local compute resources
Ollama installation

Setup time: 10-15 minutes

Best for: Development, testing, easy local setup

Requirements:

8GB+ RAM
LM Studio application
Downloaded model

Setup time: 5-10 minutes

Best for: Production use, reliability, best AI quality

Requirements:

OpenAI API account
Valid API key with credits
Internet connection

Setup time: 2-3 minutes

Best for: Enterprise, existing AI infrastructure

Requirements:

OpenAI-compatible API endpoint
Authentication credentials
Network access to API

Setup time: 5-15 minutes

OpenAI Setup

Step 1: Get Your API Key

Visit OpenAI API Platform
Create account or sign in
Navigate to API Keys (https://platform.openai.com/api-keys)
Create new secret key
Copy the key (starts with sk-)

Important: Save your API key securely. OpenAI only shows it once, and you'll need it for Gonzo configuration.

Step 2: Configure Environment

Method 1: Environment Variable (Recommended)

# Add to your ~/.bashrc, ~/.zshrc, or ~/.profile
export OPENAI_API_KEY="sk-your-actual-api-key-here"

# Reload your shell configuration
source ~/.bashrc  # or ~/.zshrc

Method 2: Session Variable

# Set for current session only
export OPENAI_API_KEY="sk-your-actual-api-key-here"

# Verify it's set
echo $OPENAI_API_KEY

Method 3: Configuration File

# Create Gonzo config file
mkdir -p ~/.config/gonzo
cat > ~/.config/gonzo/config.yml << EOF
# AI Configuration
ai-model: "gpt-4"

# Environment variables can also be set in config
# But API keys are more secure as environment variables
EOF

Step 3: Test Your Setup

# Test with automatic model selection
gonzo -f your-logs.log

# Test with specific model
gonzo -f your-logs.log --ai-model="gpt-4"

# Test with cheaper model for development
gonzo -f your-logs.log --ai-model="gpt-3.5-turbo"

Step 4: Verify AI Features Work

# 1. Start Gonzo with your logs
gonzo -f application.log --ai-model="gpt-4"

# 2. Navigate to a log entry and press 'i'
# You should see AI analysis of the log entry

# 3. Try the model switcher with 'm'
# You should see available OpenAI models

# 4. Test AI chat with 'c' in log details
# You should be able to have a conversation about the logs

Local AI Setup (Ollama)

Step 1: Install Ollama

# Install Ollama
curl -fsSL https://ollama.ai/install.sh | sh

# Verify installation
ollama --version

# Option 1: Download from website
# Visit https://ollama.ai/download and download macOS installer

# Option 2: Homebrew
brew install ollama

# Verify installation
ollama --version

# Download from https://ollama.ai/download
# Run the installer
# Open PowerShell or Command Prompt

# Verify installation
ollama --version

Step 2: Start Ollama Service

# Start Ollama server (required for Gonzo to connect)
ollama serve

# This should show:
# Ollama is running on http://localhost:11434

Keep this running: The ollama serve command needs to stay running for Gonzo to access AI features. Consider running it in a separate terminal or as a background service.

Step 3: Download AI Models

# Download recommended models for log analysis
ollama pull llama3        # Good general-purpose model (4.7GB)
ollama pull mistral       # Faster, smaller model (4.1GB)
ollama pull codellama     # Good for technical logs (3.8GB)

# Or download a larger, more capable model
ollama pull llama3:70b    # Very capable but requires 40GB+ RAM

# List available models
ollama list

Model Recommendations by System:

RAM

Recommended Model

Size

Performance

8GB

mistral

4.1GB

Good for basic analysis

16GB

llama3

4.7GB

Excellent for most use cases

32GB+

llama3:70b

40GB

Best quality, production-ready

Step 4: Configure Gonzo for Ollama

# Set environment variables for Ollama
export OPENAI_API_KEY="ollama"                    # Special key for Ollama
export OPENAI_API_BASE="http://localhost:11434"   # Ollama endpoint

# Verify Ollama is accessible
curl http://localhost:11434/api/tags

Step 5: Test Ollama Integration

# Test with automatic model selection
gonzo -f your-logs.log

# Test with specific model
gonzo -f your-logs.log --ai-model="llama3"

# Test model switching
# Press 'm' in Gonzo to see available Ollama models

Local LM Studio Setup

Step 1: Install LM Studio

Download LM Studio from https://lmstudio.ai/
Install the application for your operating system
Launch LM Studio

Step 2: Download Models

Open LM Studio
Go to "Discover" tab
Search and download recommended models:
- microsoft/DialoGPT-medium (lightweight, good for testing)
- meta-llama/Llama-2-7b-chat-hf (balanced performance)
- meta-llama/Llama-2-13b-chat-hf (better quality, needs more RAM)

Step 3: Start Model Server

Go to "Local Server" tab in LM Studio
Select your downloaded model
Click "Start Server"
Note the server URL (usually http://localhost:1234)

Step 4: Configure Gonzo for LM Studio

# Set environment variables for LM Studio
export OPENAI_API_KEY="local-key"                     # Any non-empty value
export OPENAI_API_BASE="http://localhost:1234/v1"     # Note the /v1 suffix

# Test connectivity
curl http://localhost:1234/v1/models

Step 5: Test LM Studio Integration

# Test with LM Studio
gonzo -f your-logs.log

# The model will be auto-selected from whatever's running in LM Studio
# Use 'm' to see available models

Custom API Setup

Enterprise AI Services

Azure OpenAI Service:

export OPENAI_API_KEY="your-azure-key"
export OPENAI_API_BASE="https://your-resource.openai.azure.com/"
export OPENAI_API_TYPE="azure"
export OPENAI_API_VERSION="2023-05-15"

AWS Bedrock (via compatible proxy):

export OPENAI_API_KEY="your-aws-access-key"
export OPENAI_API_BASE="https://your-bedrock-proxy.amazonaws.com/v1"

Custom OpenAI-Compatible API:

export OPENAI_API_KEY="your-custom-api-key"
export OPENAI_API_BASE="https://your-ai-service.com/v1"

Testing Custom APIs

# Test API connectivity
curl -H "Authorization: Bearer $OPENAI_API_KEY" \
     "$OPENAI_API_BASE/models"

# Test with Gonzo
gonzo -f test-logs.log --ai-model="your-model-name"

Real-World Examples

See AI features in action:

AI and a TUI: Practical Logging Tools for SREs - Practical AI use cases for incident response

Advanced Configuration

Configuration File Setup

Create a comprehensive configuration file for persistent settings:

# Create configuration directory
mkdir -p ~/.config/gonzo

# Create comprehensive config file
cat > ~/.config/gonzo/config.yml << EOF
# AI Configuration
ai-model: "gpt-4"                    # Default model
ai-auto-select: true                 # Auto-select best available model

# File and performance settings
files:
  - "/var/log/app/*.log"
follow: true
update-interval: 2s
log-buffer: 5000

# AI-specific performance settings
ai-context-size: 4000               # Maximum context for AI analysis
ai-response-timeout: 30s            # Timeout for AI responses
ai-max-retries: 3                   # Retry failed AI requests

# Development vs Production settings
profiles:
  development:
    ai-model: "gpt-3.5-turbo"       # Cheaper model for dev
    update-interval: 1s              # Faster updates
  production:
    ai-model: "gpt-4"               # Best model for production
    update-interval: 5s              # Conservative updates
EOF

Environment-Specific Configurations

Development Environment:

# ~/.config/gonzo/dev-config.yml
ai-model: "gpt-3.5-turbo"           # Cost-effective
update-interval: 1s                  # Responsive
log-buffer: 2000                     # Smaller buffer
ai-context-size: 2000               # Reduce token usage

Production Environment:

# ~/.config/gonzo/prod-config.yml
ai-model: "gpt-4"                   # Best quality
update-interval: 5s                  # Conservative
log-buffer: 10000                   # Larger buffer
ai-context-size: 8000               # More context
ai-response-timeout: 60s            # Longer timeout

Usage:

# Use specific configuration
gonzo --config ~/.config/gonzo/dev-config.yml -f logs/debug.log
gonzo --config ~/.config/gonzo/prod-config.yml -f /var/log/app.log

Multi-Provider Setup

Switching Between Providers

Script for Easy Provider Switching:

#!/bin/bash
# ai-provider-switch.sh

switch_to_openai() {
    export OPENAI_API_KEY="sk-your-openai-key"
    export OPENAI_API_BASE=""
    echo "Switched to OpenAI"
}

switch_to_ollama() {
    export OPENAI_API_KEY="ollama"
    export OPENAI_API_BASE="http://localhost:11434"
    echo "Switched to Ollama"
}

switch_to_lmstudio() {
    export OPENAI_API_KEY="local-key"
    export OPENAI_API_BASE="http://localhost:1234/v1"
    echo "Switched to LM Studio"
}

# Usage: source ai-provider-switch.sh && switch_to_openai

Provider-Specific Aliases

# Add to ~/.bashrc or ~/.zshrc

# OpenAI for production incidents
alias gonzo-openai='OPENAI_API_KEY="sk-your-key" \
                    OPENAI_API_BASE="" \
                    gonzo --ai-model="gpt-4"'

# Ollama for development
alias gonzo-local='OPENAI_API_KEY="ollama" \
                   OPENAI_API_BASE="http://localhost:11434" \
                   gonzo --ai-model="llama3"'

# LM Studio for testing
alias gonzo-lms='OPENAI_API_KEY="local-key" \
                 OPENAI_API_BASE="http://localhost:1234/v1" \
                 gonzo'

Troubleshooting Setup Issues

OpenAI Issues

"Invalid API Key" Error:

# Check API key is set correctly
echo $OPENAI_API_KEY

# Verify API key format (should start with 'sk-')
# Test API key directly
curl -H "Authorization: Bearer $OPENAI_API_KEY" \
     https://api.openai.com/v1/models

"Rate limit exceeded" Error:

# Check your OpenAI usage limits
# Visit https://platform.openai.com/account/usage

# Use cheaper model temporarily
gonzo -f logs.log --ai-model="gpt-3.5-turbo"

# Add delays between requests (automatic in Gonzo)

"Insufficient credits" Error:

# Add credits to your OpenAI account
# Visit https://platform.openai.com/account/billing

# Check current balance
curl -H "Authorization: Bearer $OPENAI_API_KEY" \
     https://api.openai.com/v1/dashboard/billing/subscription

Ollama Issues

"Connection refused" Error:

# Check if Ollama is running
ps aux | grep ollama

# Start Ollama if not running
ollama serve

# Check if port is accessible
curl http://localhost:11434/api/tags

"Model not found" Error:

# List available models
ollama list

# Download missing model
ollama pull llama3

# Verify model is available
ollama show llama3

High Memory Usage:

# Use smaller model
ollama pull mistral  # 4.1GB instead of 7GB+

# Monitor memory usage
htop

# Restart Ollama if memory issues persist
killall ollama
ollama serve

LM Studio Issues

"No models available" Error:

# Check LM Studio is running with server started
# Verify model is loaded in LM Studio
# Check server URL is correct (usually :1234)

# Test connectivity
curl http://localhost:1234/v1/models

"Model loading failed" Error:

# Check available RAM
free -h

# Try smaller model in LM Studio
# Restart LM Studio application
# Verify model file isn't corrupted

Network and Firewall Issues

Corporate Firewall:

# Test connectivity to AI providers
curl -I https://api.openai.com
curl -I http://localhost:11434  # For Ollama
curl -I http://localhost:1234   # For LM Studio

# Configure proxy if needed
export HTTP_PROXY=http://your-proxy:port
export HTTPS_PROXY=http://your-proxy:port

SSL Certificate Issues:

# Skip SSL verification (not recommended for production)
export PYTHONHTTPSVERIFY=0

# Or install proper certificates
# Contact your IT department for certificate installation

What's Next?

Now that you have AI configured, explore these guides:

AI Providers Guide - Detailed setup for specific providers
Using AI Features - Master AI-powered workflows and analysis
Log Analysis - Combine AI with algorithmic analysis

Or start using AI features immediately:

# Start analyzing with AI
gonzo -f your-logs.log --ai-model="gpt-4"

# Try these AI features:
# - Press 'i' on any log entry for analysis
# - Press 'c' in log details for interactive chat
# - Press 'm' to switch between AI models

Your AI-powered log analysis is ready! 🤖 You can now get intelligent insights, natural language explanations, and automated pattern recognition for any log analysis task.

Last updated 1 month ago