llm-checker
Version:
Intelligent CLI tool with AI-powered model selection that analyzes your hardware and recommends optimal LLM models for your system
214 lines (176 loc) • 8.17 kB
JavaScript
const AIModelSelector = require('./index');
const { spawn } = require('child_process');
class OllamaAISelector {
constructor() {
this.selector = new AIModelSelector();
}
async getAvailableModels() {
return new Promise((resolve, reject) => {
try {
const ollama = spawn('ollama', ['list']);
let output = '';
let error = '';
ollama.stdout.on('data', (data) => {
output += data.toString();
});
ollama.stderr.on('data', (data) => {
error += data.toString();
});
ollama.on('close', (code) => {
if (code !== 0) {
reject(new Error(`Ollama list failed: ${error}`));
return;
}
// Parse ollama list output
const lines = output.trim().split('\n').slice(1); // Skip header
const models = lines
.filter(line => line.trim())
.map(line => line.split(/\s+/)[0])
.filter(model => model && !model.includes('NAME'));
resolve(models);
});
ollama.on('error', (err) => {
if (err.code === 'ENOENT') {
reject(new Error('Ollama not found. Please install Ollama from https://ollama.ai'));
} else {
reject(new Error(`Ollama spawn error: ${err.message}`));
}
});
} catch (spawnError) {
reject(new Error(`Failed to start Ollama: ${spawnError.message}`));
}
});
}
async runOllamaModel(modelName, prompt) {
console.log(`🚀 Running Ollama with ${modelName}...`);
return new Promise((resolve, reject) => {
try {
const args = ['run', modelName];
if (prompt) {
args.push(prompt);
}
const ollama = spawn('ollama', args, {
stdio: 'inherit' // Stream output directly to terminal
});
ollama.on('close', (code) => {
if (code === 0) {
resolve();
} else {
reject(new Error(`Ollama exited with code ${code}`));
}
});
ollama.on('error', (error) => {
if (error.code === 'ENOENT') {
reject(new Error('Ollama not found. Please install Ollama from https://ollama.ai'));
} else {
reject(new Error(`Failed to start Ollama: ${error.message}`));
}
});
} catch (spawnError) {
reject(new Error(`Failed to start Ollama: ${spawnError.message}`));
}
});
}
async selectAndRun(candidateModels = null, prompt = null) {
try {
console.log('🧠 AI-Powered Ollama Model Selector');
console.log('=' * 40);
// Get available models if not provided
if (!candidateModels) {
console.log('📋 Getting available models...');
candidateModels = await this.getAvailableModels();
if (candidateModels.length === 0) {
console.log('❌ No Ollama models found. Install some models first:');
console.log(' ollama pull llama2:7b');
console.log(' ollama pull mistral:7b');
console.log(' ollama pull phi3:mini');
return;
}
console.log(`✅ Found ${candidateModels.length} models: ${candidateModels.join(', ')}`);
}
// Use AI to select best model
let result;
try {
console.log('🔍 Analyzing your hardware...');
result = await this.selector.predictBestModel(candidateModels);
console.log('✅ AI selection completed');
} catch (error) {
console.log(`⚠️ AI selection failed: ${error.message}`);
console.log('🔄 Falling back to heuristic selection...');
const specs = await this.selector.getSystemSpecs();
result = this.selector.selectModelHeuristic(candidateModels, specs);
}
// Display results
console.log('\n📊 Selection Results:');
console.log(`🏆 Best Model: ${result.bestModel}`);
if (result.systemSpecs) {
const specs = result.systemSpecs;
console.log(`💻 System: ${specs.cpu_cores} cores, ${specs.total_ram_gb.toFixed(1)}GB RAM`);
console.log(`🎮 GPU: ${specs.gpu_model_normalized} (${specs.gpu_vram_gb.toFixed(1)}GB VRAM)`);
}
if (result.allPredictions) {
console.log('\n📈 All Model Scores:');
result.allPredictions.slice(0, 5).forEach((pred, i) => {
const score = (pred.score * 100).toFixed(1);
const icon = i === 0 ? '🥇' : i === 1 ? '🥈' : i === 2 ? '🥉' : ' ';
console.log(`${icon} ${pred.model}: ${score}% (${pred.size}B params)`);
});
}
// Run the selected model
console.log(`\n🚀 Launching ${result.bestModel}...`);
await this.runOllamaModel(result.bestModel, prompt);
} catch (error) {
console.error('❌ Error:', error.message);
process.exit(1);
}
}
}
// CLI interface
async function main() {
const args = process.argv.slice(2);
if (args.includes('--help') || args.includes('-h')) {
console.log('AI-Powered Ollama Model Selector');
console.log('');
console.log('Automatically selects the best Ollama model for your hardware using AI.');
console.log('');
console.log('Usage:');
console.log(' ollama-select # Select from all available models');
console.log(' ollama-select model1 model2 # Select from specific models');
console.log(' ollama-select --test # Test the selector');
console.log('');
console.log('Examples:');
console.log(' ollama-select llama2:7b mistral:7b phi3:mini');
console.log(' ollama-select');
console.log('');
return;
}
if (args.includes('--test')) {
console.log('🧪 Testing AI Model Selector...');
const selector = new AIModelSelector();
try {
await selector.initialize();
const testModels = ['llama2:7b', 'mistral:7b', 'phi3:mini'];
const result = await selector.predictBestModel(testModels);
console.log('✅ Test successful!');
console.log(`Best model: ${result.bestModel}`);
console.log('Scores:', result.allPredictions.map(p => `${p.model}: ${(p.score * 100).toFixed(1)}%`));
} catch (error) {
console.log('❌ Test failed:', error.message);
// Test fallback
console.log('🔄 Testing fallback heuristic...');
const result = selector.selectModelHeuristic(testModels);
console.log(`Fallback result: ${result.bestModel} (${result.reason})`);
}
return;
}
// Extract model candidates and prompt
const candidateModels = args.filter(arg => !arg.startsWith('--'));
const prompt = null; // Could be extended to accept prompts
const cli = new OllamaAISelector();
await cli.selectAndRun(candidateModels.length > 0 ? candidateModels : null, prompt);
}
if (require.main === module) {
main().catch(console.error);
}
module.exports = OllamaAISelector;