Welcome to the lesson on creating the LLM Manager in TypeScript, a crucial component of the AI Cooking Helper project. In previous lessons, you learned about the prompts module and how to make basic LLM calls. Now, we will focus on the LLM Manager, which facilitates interactions with language models like OpenAI's GPT. This manager is responsible for rendering prompts, sending them to the language model, and handling the responses. By the end of this lesson, you will understand how to set up and use the LLM Manager effectively in a TypeScript project.

In previous units, we learned how to set up the OpenAI Client to make requests:

Remember, in the Codesignal environment the variables needed like the API key are already configured for you, do not worry!

The generateResponse function is central to the LLM Manager. It renders system and user prompts, sends them to the language model, and returns the response. Let's break it down step by step.

First, we need to render the system and user prompts using the renderPromptFromFile function, which was covered in a previous lesson.

• system and user are generated by calling renderPromptFromFile with the respective prompt names and variables. This function replaces placeholders in the prompt templates with actual values.

Next, we send the rendered prompts to the language model using the client.

We use the client.chat.completions.create method to send the prompts, like we saw in past units:

• The model parameter specifies which language model to use, such as gpt-4o.
• The messages parameter contains the system and user prompts.
• The temperature parameter controls the randomness of the response.

Error handling is crucial when interacting with APIs. The LLM Manager includes error handling and logging to manage unexpected issues.

In TypeScript, we use a try/catch block to handle errors and log them using console.error.

• We catch any errors that occur during the process.
• We log the error message using console.error.
• If an error occurs, we return null to indicate that no response was generated.

Here is the complete implementation of the LLM Manager in TypeScript, combining all the concepts discussed in this lesson:

This implementation sets up the OpenAI client, renders prompts, sends them to the language model, handles errors, and logs important information — all in one place, using TypeScript best practices.

In this lesson, you learned how to create the LLM Manager in TypeScript, a key component of the AI Cooking Helper. We covered setting up the OpenAI client, understanding the generateResponse function, and implementing error handling and logging. These skills are essential for managing interactions with language models effectively.

As you move on to the practice exercises, you'll have the opportunity to apply what you've learned. Experiment with different prompt inputs and model parameters to see how they affect the responses. Congratulations on reaching this point in the course, and keep up the great work as you continue to build your AI Cooking Helper!