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docker desktop enable model-runner --tcp 12434
docker model pull ai/gemma3

// config.js

export default {
  // Model configuration
  openai: {
    baseURL: "http://localhost:12434/engines/v1", // Base URL for Docker Model Runner
    apiKey: 'ignored',
    model: "ai/gemma3",
    commentGeneration: { // Each task has its own configuration, for example temperature is set to a high value when generating comments for creativity
      temperature: 0.3, 
      max_tokens: 250,
      n: 1,
    },
    embedding: {
      model: "ai/mxbai-embed-large", // Model for generating embeddings
    },
  },
  // ... other configuration options
};

import OpenAI from 'openai';
import config from './config.js';

// Initialize OpenAI client with local endpoint
const client = new OpenAI({
  baseURL: config.openai.baseURL,
  apiKey: config.openai.apiKey,
});

/**
 * Create a prompt for comment generation
 * @param {string} type - Type of comment (positive, negative, neutral)
 * @param {string} topic - Topic of the comment
 * @returns {string} - Prompt for OpenAI
 */
function createPromptForCommentGeneration(type, topic) {
  let sentiment = '';
  
  switch (type) {
    case 'positive':
      sentiment = 'positive and appreciative';
      break;
    case 'negative':
      sentiment = 'negative and critical';
      break;
    case 'neutral':
      sentiment = 'neutral and balanced';
      break;
    default:
      sentiment = 'general';
  }
  
  return `Generate a realistic ${sentiment} user comment about an AI assistant called Jarvis, focusing on its ${topic}.
  
The comment should sound natural, as if written by a real user who has been using Jarvis.
Keep the comment concise (1-3 sentences) and focused on the specific topic.
Do not include ratings (like "5/5 stars") or formatting.
Just return the comment text without any additional context or explanation.`;
}

"Honestly, Jarvis is just a lot of empty promises. It keeps suggesting irrelevant articles and failing to actually understand my requests for help with my work – it’s not helpful at all."

"Jarvis is seriously impressive – the speed at which it responds is incredible! I’ve never used an AI assistant that’s so quick and efficient, it’s a game changer.

const response = await client.chat.completions.create({
    model: config.openai.model,
    messages: [
      {
        role: "system",
        content: `You are a customer support representative for an AI assistant called Jarvis. Your task is to generate polite, helpful responses to user comments.

Guidelines for responses:
1. Be empathetic and acknowledge the user's feedback
2. Thank the user for their input
3. If the comment is positive, express appreciation
4. If the comment is negative, apologize for the inconvenience and assure them you're working on improvements
5. If the comment is neutral, acknowledge their observation
6. If relevant, mention that their feedback will be considered for future updates
7. Keep responses concise (2-4 sentences) and professional
8. Do not make specific promises about feature implementation or timelines
9. Sign the response as "The Jarvis Team"`
      },
      {
        role: "user",
        content: `User comment: "${comment.text}"
Comment category: ${comment.category || 'unknown'}

${featuresContext}

Generate a polite, helpful response to this user comment.`
      }
    ],
    temperature: 0.7,
    max_tokens: 200
  });

Thank you so much for your positive feedback regarding Jarvis’s interface! We’re thrilled to hear you find it clean and intuitive – that’s exactly what we’re aiming for. We appreciate you pointing out your desire for more visual customization options, and your feedback will definitely be considered as we continue to develop Jarvis.

The Jarvis Team

Thank you for your feedback – we appreciate you taking the time to share your observations about Jarvis. We sincerely apologize for the glitches and freezes you’ve experienced; we understand how frustrating that can be. Your input is valuable, and we’re actively working on improvements to enhance Jarvis’s reliability and accuracy. 

The Jarvis Team

/**
 * Extract features from comments
 * @param {string} commentsText - Text of comments
 * @returns {Promise<Array>} - Array of identified features
 */
async function extractFeaturesFromComments(commentsText) {
  const response = await client.chat.completions.create({
    model: config.openai.model,
    messages: [
      {
        role: "system",
        content: `You are a product analyst for an AI assistant called Jarvis. Your task is to identify potential product features or improvements based on user comments.
        
For each set of comments, identify up to 3 potential features or improvements that could address the user feedback.

For each feature, provide:
1. A short name (2-5 words)
2. A brief description (1-2 sentences)
3. The type of feature (New Feature, Improvement, Bug Fix)
4. Priority (High, Medium, Low)

Format your response as a JSON array of features, with each feature having the fields: name, description, type, and priority.`
      },
      {
        role: "user",
        content: `Here are some user comments about Jarvis. Identify potential features or improvements based on these comments:

${commentsText}`
      }
    ],
    response_format: { type: "json_object" },
    temperature: 0.5
  });
  
  try {
    const result = JSON.parse(response.choices[0].message.content);
    return result.features || [];
  } catch (error) {
    console.error('Error parsing feature identification response:', error);
    return [];
  }
}

"features": [
    {
      "name": "Enhanced Visual Customization",
      "description": "Allows users to personalize the Jarvis interface with more themes, icon styles, and display options to improve visual appeal and user preference.",
      "type": "Improvement",
      "priority": "Medium",
      "clusters": [
        "1"
      ]
    },