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/**
* Exercise 9 Solution: Basic LLM Setup
*/
import {HumanMessage, SystemMessage, LlamaCppLLM} from '../../../../src/index.js';
import {QwenChatWrapper} from "node-llama-cpp";
async function exercise1() {
console.log('=== Exercise 1: Basic LLM Setup ===\n');
// Solution: Create a LlamaCppLLM instance
const llm = new LlamaCppLLM({
modelPath: './models/Qwen3-1.7B-Q6_K.gguf', // Adjust to your model path
temperature: 0.7,
maxTokens: 100,
chatWrapper: new QwenChatWrapper({
thoughts: 'discourage' // Prevents the model from outputting thinking tokens
}),
verbose: true // Enable to see model loading
});
try {
// Part 1: Simple string invocation
console.log('Part 1: Simple string input');
const response1 = await llm.invoke("What is 2+2? Answer in one sentence");
console.log('Response:', response1.content);
console.log();
// Part 2: Using message objects
console.log('Part 2: Using message objects');
const messages = [
new SystemMessage("You are a patient math tutor teaching a 10-year-old. Always explain the reasoning step-by-step in simple terms."),
new HumanMessage("What is 5*5? Answer in one sentence.")
];
const response2 = await llm.invoke(messages);
console.log('Response:', response2.content);
console.log();
// Part 3: Temperature experimentation
console.log('Part 3: Temperature differences');
console.log('Temperature controls randomness: 0.0 = deterministic, 1.0 = creative\n');
const question = "Give me one adjective to describe winter:";
console.log('Low temperature (0.1):');
llm._chatSession.setChatHistory([]);
const lowTemp = await llm.invoke(question, { temperature: 0.1 });
// Should always return the same word: "cold"
console.log(lowTemp.content);
console.log('\nHigh temperature (0.9):');
llm._chatSession.setChatHistory([]);
const highTemp = await llm.invoke(question, { temperature: 0.9 });
// Different each time: "frosty", "snowy", "chilly", "icy", "freezing"
console.log(highTemp.content);
} finally {
// Cleanup: Always dispose when done
await llm.dispose();
console.log('\n✓ Resources cleaned up');
}
console.log('\n✓ Exercise 1 complete!');
}
// Run the solution
exercise1().catch(console.error);
/**
* Key Takeaways:
*
* 1. Construction:
* - modelPath is required
* - Other options have sensible defaults
* - Set verbose: true to see what's happening
*
* 2. Input flexibility:
* - Strings work great for simple queries
* - Message arrays give you full control
* - Both approaches return AIMessage objects
*
* 3. Runtime configuration:
* - Pass config as second parameter to invoke()
* - Overrides instance defaults for that call only
* - Useful for different behaviors per query
*
* 4. Resource management:
* - Always call dispose() when done
* - Use try/finally to ensure cleanup
* - Models hold significant memory
*/
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