Building Your Own ChatGPT Clone

Repository: https://github.com/rexposadas/chatbot

Have you ever wondered how ChatGPT works behind the scenes? In this post, I’ll walk you through creating your own simplified ChatGPT-like application using Go and modern AI technologies.

Project Overview

This project creates a simple but functional chatbot that leverages OpenAI’s API to generate responses. The architecture is straightforward but powerful:

1. A Go backend that handles API requests
2. Integration with OpenAI’s GPT models
3. A clean web interface for user interaction
4. Message history management

Key Components

Backend API

The heart of our chatbot is the Go backend that handles requests and communicates with OpenAI:

func handleChat(w http.ResponseWriter, r *http.Request) {
    // Parse the incoming request
    var req ChatRequest
    if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
        http.Error(w, err.Error(), http.StatusBadRequest)
        return
    }
    
    // Call OpenAI API
    response, err := callOpenAI(req.Messages)
    if err != nil {
        http.Error(w, "Failed to get response from AI", http.StatusInternalServerError)
        return
    }
    
    // Return the response
    json.NewEncoder(w).Encode(ChatResponse{
        Message: response,
    })
}

OpenAI Integration

The magic happens when we connect to OpenAI’s powerful language models:

func callOpenAI(messages []Message) (string, error) {
    client := openai.NewClient(os.Getenv("OPENAI_API_KEY"))
    
    // Convert our messages to OpenAI format
    var chatMessages []openai.ChatCompletionMessage
    for _, msg := range messages {
        role := openai.ChatMessageRoleUser
        if msg.Role == "assistant" {
            role = openai.ChatMessageRoleAssistant
        }
        
        chatMessages = append(chatMessages, openai.ChatCompletionMessage{
            Role:    role,
            Content: msg.Content,
        })
    }
    
    // Make the API call
    resp, err := client.CreateChatCompletion(
        context.Background(),
        openai.ChatCompletionRequest{
            Model:    "gpt-3.5-turbo",
            Messages: chatMessages,
        },
    )
    
    if err != nil {
        return "", err
    }
    
    return resp.Choices[0].Message.Content, nil
}

Web Interface

The frontend is built with simple HTML, CSS, and JavaScript to create a clean, responsive interface:

How to Run the Project

Getting started is simple:

1. Clone the repository:

   git clone https://github.com/rexposadas/chatgpt.git
   cd chatgpt
   

2. Set up your OpenAI API key:

   export OPENAI_API_KEY="your-api-key-here"
   

3. Build and run the application:

   go build
   ./chatgpt
   

4. Open your browser and navigate to http://localhost:8080

Customization Options

The beauty of building your own chatbot is the ability to customize it. Here are some ideas:

Change the AI Model

You can easily switch between different OpenAI models by modifying the model parameter:

resp, err := client.CreateChatCompletion(
    context.Background(),
    openai.ChatCompletionRequest{
        Model:    "gpt-4", // Try different models here
        Messages: chatMessages,
    },
)

Add System Instructions

To give your chatbot a specific personality or role, add a system message:

chatMessages = append([]openai.ChatCompletionMessage{
    {
        Role:    openai.ChatMessageRoleSystem,
        Content: "You are a helpful assistant specialized in programming advice.",
    },
}, chatMessages...)

Implement Message History

For a more coherent conversation, implement message history storage:

type Conversation struct {
    ID       string    `json:"id"`
    Messages []Message `json:"messages"`
    Created  time.Time `json:"created"`
}

func saveConversation(conv Conversation) error {
    // Save to database or file
    data, err := json.Marshal(conv)
    if err != nil {
        return err
    }
    
    return os.WriteFile("conversations/"+conv.ID+".json", data, 0644)
}

Challenges and Solutions

Building a chatbot comes with its challenges:

1. API Rate Limits: OpenAI has rate limits that can affect your application during heavy usage. Implement a queue system for handling requests during high traffic.

2. Token Management: GPT models have token limits. Implement a system to manage conversation length by summarizing or truncating older messages.

3. Cost Management: API calls cost money. Consider implementing caching for common questions or using a tiered approach with simpler models for basic queries.

Next Steps

This project provides a solid foundation, but there’s always room for improvement:

1. Add user authentication
2. Implement conversation management
3. Add support for file uploads and processing
4. Create a mobile app version

Conclusion

Building your own ChatGPT clone is not only educational but also gives you complete control over your AI assistant. You can customize it to your specific needs, add domain-specific knowledge, and integrate it with your existing systems.

The code is available on GitHub - feel free to fork it, improve it, and make it your own!

Happy coding!