LLM Integration

// npm install gravixlayer
import { GravixLayer, Sandbox } from 'gravixlayer';

// Create Gravix client
const client = new GravixLayer();

const system = "You are a helpful assistant that can execute python code. Only respond with the code to be executed and nothing else. Strip backticks in code blocks.";
const prompt = "Calculate how many r's are in the word 'strawberry'";

// Send messages to Gravix Inference API
const response = await client.chat.completions.create({
    model: "meta-llama/llama-3.1-8b-instruct",
    messages: [
        { role: "system", content: system },
        { role: "user", content: prompt }
    ]
});

// Extract the code from the response
const llmContent = response.choices[0].message.content;

// Clean up code block formatting (strip backticks)
const code = llmContent.replace(/```python/g, "").replace(/```/g, "").trim();

console.log(`Generated Code:
${code}
`);

// Execute code in Gravix AgentBox
if (code) {
    // Create a sandbox
    const sandbox = await Sandbox.create();
    console.log(`Created Sandbox: ${sandbox.sandbox_id}`);
    
    try {
        // Run the code
        const execution = await sandbox.runCode(code);
        
        // Print results
        if (execution.logs.stdout && execution.logs.stdout.length > 0) {
            console.log("Output:", execution.logs.stdout.join('\n'));
        }
        if (execution.logs.stderr && execution.logs.stderr.length > 0) {
            console.log("Error:", execution.logs.stderr.join('\n'));
        }
    } finally {
        // Cleanup
        await sandbox.kill();
        console.log("Sandbox deleted");
    }
}