In the previous lesson, we explored the SessionManager class, which is responsible for managing tutoring session data within our application. Now, we will take the next step by building the Tutor Service Layer. This layer is essential for integrating a language model with tutoring sessions, allowing us to process student queries and generate tailored explanations. By the end of this lesson, you will understand how to set up the TutorService class, create tutoring sessions, and process academic questions using a language model.

The service layer acts as a bridge between the part of the application that manages data and the part that generates educational responses. It is responsible for orchestrating the flow of data and ensuring that student interactions are handled effectively. Let's dive into the details of setting up this important component.

The TutorService class is the heart of our service layer. It is responsible for managing tutoring sessions and interacting with the language model to generate educational responses. To begin, we need to set up the class and its components.

First, we need to make sure we have access to the SessionManager from our previous lesson. We also need a way to make requests to the language model's API and a method to load the system prompt from a file. Here's how the class is initialized:

In this setup, we instantiate SessionManager to manage tutoring data, set up a Guzzle HTTP client for API requests, and load the systemPrompt using the loadSystemPrompt method, which we'll discuss next.

The system prompt is a crucial component that guides the tutor's responses. It provides context and instructions, ensuring that the tutor behaves like an effective educational assistant. In this section, we'll implement the loadSystemPrompt method to load the prompt from a file.

This method attempts to read the system prompt from a specified file path. If successful, it returns the prompt as a string. If the file is missing or cannot be read, it returns a default prompt. This ensures that the application can continue functioning even if the file is missing or corrupted.

Creating a new tutoring session is a fundamental task of the TutorService. The createSession method is responsible for generating a unique session ID and initializing a tutoring session using the SessionManager.

In this method, we generate a unique sessionId using a unique identifier function. We then call the createSession method of SessionManager, passing the studentId, sessionId, and systemPrompt. This initializes a new tutoring session, which is ready to receive student queries.

The processQuery method is where the educational magic happens. It processes student questions, interacts with the language model to generate tutoring explanations, and updates the session history. Below, we outline the steps involved in this process, followed by the corresponding code implementation:

1. Retrieve the session using getSession, and raise an error if the session is not found.
2. Add the student's query to the session history.
3. Send the conversation, including the system prompt and all previous exchanges, to the language model to generate a response.
4. Add the tutor's explanation to the session history and return it to the student.
5. Handle any errors with the API client gracefully.

In this process, we configure the language model with specific parameters to optimize its educational performance. The temperature is set to 0.7, which balances accuracy and creativity in the tutor's explanations, ensuring they are both informative and engaging. The max_tokens is set to 500, allowing the model to provide detailed educational content without overwhelming the student, thus maintaining an effective learning experience.

Let's see the TutorService in action by simulating a tutoring session. We'll create a script to initialize a tutoring session and process a student's academic query.

In this example, we initialize the TutorService, simulate a student ID, and create a new tutoring session, printing the session ID. We then simulate sending an academic question and print the tutor's response, demonstrating the flow from student query to tutoring explanation and showcasing the functionality of the TutorService.

In this lesson, we explored the TutorService class and its role in integrating a language model with tutoring sessions. We learned how to set up the class, load the system prompt, create sessions, and process student queries. The service layer is a vital component of our personal tutor application, ensuring that student interactions are handled effectively and that educational content is delivered in a clear and engaging manner.

As you move on to the practice exercises, take the opportunity to experiment with the TutorService functionality. This hands-on practice will reinforce the concepts covered in this lesson and prepare you for the next steps in our course. Keep up the great work, and I look forward to seeing your progress!