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You can create a personalised AI Companion using Zentry. This guide will walk you through the necessary steps and provide the complete code to get you started.

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Overview

The Personalized AI Companion leverages Zentry to retain information across interactions, enabling a tailored learning experience. It creates memories for each user interaction and integrates with OpenAI’s GPT models to provide detailed and context-aware responses to user queries.

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Setup

Before you begin, ensure you have Node.js installed and create a new project. Install the required dependencies using npm: 
npm install openai Zentryai

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Full Code Example

Below is the complete code to create and interact with an AI Companion using Zentry: 
import { OpenAI } from 'openai';
import { Memory } from 'Zentryai/oss';
import * as readline from 'readline';

const openaiClient = new OpenAI();
const memory = new Memory();

async function chatWithMemories(message, userId = "default_user") {
  const relevantMemories = await memory.search(message, { userId: userId });
  
  const memoriesStr = relevantMemories.results
    .map(entry => `- ${entry.memory}`)
    .join('\n');
  
  const systemPrompt = `You are a helpful AI. Answer the question based on query and memories.
User Memories:
${memoriesStr}`;
  
  const messages = [
    { role: "system", content: systemPrompt },
    { role: "user", content: message }
  ];
  
  const response = await openaiClient.chat.completions.create({
    model: "gpt-4o-mini",
    messages: messages
  });
  
  const assistantResponse = response.choices[0].message.content || "";
  
  messages.push({ role: "assistant", content: assistantResponse });
  await memory.add(messages, { userId: userId });
  
  return assistantResponse;
}

async function main() {
  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout
  });
  
  console.log("Chat with AI (type 'exit' to quit)");
  
  const askQuestion = () => {
    return new Promise((resolve) => {
      rl.question("You: ", (input) => {
        resolve(input.trim());
      });
    });
  };
  
  try {
    while (true) {
      const userInput = await askQuestion();
      
      if (userInput.toLowerCase() === 'exit') {
        console.log("Goodbye!");
        rl.close();
        break;
      }
      
      const response = await chatWithMemories(userInput, "sample_user");
      console.log(`AI: ${response}`);
    }
  } catch (error) {
    console.error("An error occurred:", error);
    rl.close();
  }
}

main().catch(console.error);

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Key Components

  1. Initialization
    • The code initializes both OpenAI and Zentry Memory clients
    • Uses Node.js’s built-in readline module for command-line interaction
  2. Memory Management (chatWithMemories function)
    • Retrieves relevant memories using Zentry’s search functionality
    • Constructs a system prompt that includes past memories
    • Makes API calls to OpenAI for generating responses
    • Stores new interactions in memory
  3. Interactive Chat Interface (main function)
    • Creates a command-line interface for user interaction
    • Handles user input and displays AI responses
    • Includes graceful exit functionality

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Environment Setup

Make sure to set up your environment variables: 
export OPENAI_API_KEY=your_api_key

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Conclusion

This implementation demonstrates how to create an AI Companion that maintains context across conversations using Zentry’s memory capabilities. The system automatically stores and retrieves relevant information, creating a more personalized and context-aware interaction experience. As users interact with the system, Zentry’s memory system continuously learns and adapts, making future responses more relevant and personalized. This setup is ideal for creating long-term learning AI assistants that can maintain context and provide increasingly personalized responses over time.