Exploring The Capabilities Of Codex - A Module On Azure OpenAI's AI-Powered Code Generator

If you are new to this module, we recommend reading the Azure OpenAI module before proceeding to the Codex module. In our previous module, we covered Davinci, and in this post, we will delve into the Codex module.

Codex for code understanding and generation (including translation from natural language to code), and Embeddings for utilizing and understanding semantic representations of text data.

Possible ways to use Codex in different scenarios

Codex can be used in various scenarios where there is a need to generate code quickly and efficiently. Here are some examples of how Codex can be used:

Code Autocompletion

Codex can be used as an intelligent code autocompletion tool. Developers can use Codex to suggest code snippets and autocomplete code blocks based on the context of their current code.

Code Refactoring

Codex can also be used to refactor code. Developers can provide natural language input to Codex, describing how they would like to modify their existing code, and Codex can generate the necessary code to make those changes.

Code Generation

Codex can be used to generate code for various purposes. For instance, it can generate code for machine learning models, web applications, mobile applications, or any other software program.

Prototyping

Codex can be used to prototype ideas for new projects quickly. Developers can describe their project ideas in natural language input, and Codex can generate the initial code framework.

Education

Codex can be used to teach programming to beginners. Codex can generate code for various programming concepts, and beginners can learn by exploring the code generated by Codex.

Overall, Codex has the potential to revolutionize the way developers write code and can be used in various scenarios where there is a need to generate code quickly and efficiently.

In this example, I will demonstrate how to generate code using Codex by sending a natural language request. The request is to create a Python function that takes in two numbers and returns their sum.

Make sure you are using model "text-davinci-002"

To use C# language, I have created a Console application using Visual Studio 2022.

Send the request to Azure OpenAI to generate the code initially.

var request = "Create a function in Python that takes in two numbers and returns their sum.";
var result = await ConnectAzureOpenAI(Url, key, request);
Console.WriteLine(result);

The module's response is obtained as shown below.

If this code appears inadequate, submit another request with more detailed information. For instance, request to add a try-catch feature to the code this time.

request = "How about adding try catch to the function that takes in two numbers and returns their sum.?";       
result = await ConnectAzureOpenAI(Url, key, request);
Console.WriteLine(result);

This time module's response is obtained as shown below

private static async Task < string > ConnectAzureOpenAI(string url, string key, string message) {
    string summary = string.Empty;
    try {
        using
        var client = new HttpClient();
        client.BaseAddress = new Uri(url);
        client.DefaultRequestHeaders.Accept.Add(new MediaTypeWithQualityHeaderValue("application/json"));
        client.DefaultRequestHeaders.Add("api-key", key);
        var jsonContent = new {
            prompt = message,
                max_tokens = 1024,
                temperature = 0.7,
                frequency_penalty = 0.5,
                presence_penalty = 0,
                top_p = 0.5,
                best_of = 1
        };
        var msg = new HttpRequestMessage(HttpMethod.Post, url);
        var prompt = new StringContent(JsonConvert.SerializeObject(jsonContent), Encoding.UTF8, "application/json");
        msg.Content = prompt;
        var res = await client.SendAsync(msg).ConfigureAwait(false);
        if (res.IsSuccessStatusCode) {
            // Read the response content as a string
            var stringResult = await res.Content.ReadAsStringAsync().ConfigureAwait(false);
            // Parse the string result as a JObject
            var result = JObject.Parse(stringResult);
            // If the "choices" key exists in the result and it has at least one value
            if (result["choices"] != null && result["choices"].Any()) {
                // Extract the summary text from the first choice in the "choices" array
                summary = result["choices"][0]["text"].ToString();
            }
        } else {
            summary = "Request has been failed";
        }
    } catch (Exception e) {
        summary = e.Message;
    }
    return summary;
}

I hope that with this module, you have understood the codex for using Azure OpenAI.