In the previous lesson, we explored the SessionManager class, which plays a crucial role in managing tutoring session data within our application. Now, we will take the next step in our journey by building the Tutor Service Layer. This layer is essential for integrating the DeepSeek 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 DeepSeek models via HTTP requests.

The service layer acts as a bridge between the model layer, where data is managed, and the AI model, which 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 DeepSeek model to generate educational responses. To begin, we need to set up the class and its components.

First, we include the necessary namespaces, such as System, System.IO, System.Net.Http, and any project-specific namespaces for session management. We also use Guid.NewGuid() to generate unique session IDs. Here’s how the class is initialized in C#:

In this setup, we instantiate SessionManager to manage tutoring data, initialize an HttpClient for making HTTP requests to the DeepSeek API, and load the systemPrompt using the LoadSystemPrompt method, which we'll discuss next.

The ProcessQuery method is where the educational magic happens. It processes student questions, interacts with the DeepSeek 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 throw an exception 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 DeepSeek model via an HTTP request 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 HTTP client gracefully.

Let's see the TutorService in action by simulating multiple tutoring sessions for the same student. We'll create a simple program to initialize two tutoring sessions and process a student's academic query in each session.

In this example, we initialize the TutorService, simulate a student ID, and create two separate tutoring sessions for the same student, printing the session IDs. We then simulate sending a question in each session and print the AI's response, demonstrating the flow from student query to tutoring explanation and showcasing the functionality of the TutorService. Each session operates independently, ensuring that the context of one session does not affect the other.

In this lesson, we explored the TutorService class and its role in integrating the DeepSeek 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!