// Must precede any llm module imports
import * as Langtrace from "@langtrase/typescript-sdk";
Langtrace.init({ api_key: "<LANGTRACE_API_KEY>" });

import * as Langtrace from '@langtrace/typescript-sdk';
import OpenAI from 'openai';

// Initialize dotenv
import dotenv from 'dotenv'
dotenv.config()

// Initialize Langtrace SDK
Langtrace.init()

const openai = new OpenAI()

// Function to handle OpenAI interaction and user feedback
export const run = async (): Promise<void> => {
  await Langtrace.withLangTraceRootSpan(async (spanId, traceId) => {
    const response = await openai.chat.completions.create({
      model: 'gpt-4o-mini',
      messages: [
        { role: 'system', content: 'Talk like a pirate' },
        { role: 'user', content: 'Tell me a story in 3 sentences or less.' }
      ],
      stream: false
    })

    // Send traceId and spanId to your client
    // Collect user feedback (This is likely going to be another route in your application) For this example, we are sending the feedback immediately after the interaction
    const userScore = 5 // Example user score
    const userId = 'user123' // Example user ID
    await Langtrace.sendUserFeedback({ spanId, traceId, userScore, userId })
  })
}

import * as Langtrace from "@langtrase/typescript-sdk";
await Langtrace.sendUserFeedback({spanId, traceId, userScore, userId});