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 the OpenAI SDK.

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 import the necessary modules, including the SessionManager from our previous lesson and the OpenAI client (which we'll use to access DeepSeek models). We also use the uuid module to generate unique session IDs. Here's how the class is initialized:

In this setup, we instantiate SessionManager to manage tutoring data, initialize the OpenAI client for DeepSeek model access, and load the system_prompt using the load_system_prompt method, which we'll discuss next.

The system prompt is a crucial component that guides the tutor AI's responses. It provides context and instructions for the AI, ensuring that it behaves like an effective educational assistant. In this section, we'll implement the load_system_prompt 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. In case of an error, it prints an error message and 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 create_session method is responsible for generating a unique session ID and initializing a tutoring session using the SessionManager.

In this method, we generate a unique session_id using the uuid module. We then call the create_session method of SessionManager, passing the student_id, session_id, and system_prompt. This initializes a new tutoring session, which is ready to receive student queries.

The process_query 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 get_session, 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 DeepSeek 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 AI client gracefully.

In the context of a personal tutor, we configure our DeepSeek model with specific parameters to optimize its educational performance. The temperature is set to 0.6, 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 economics question and print the tutor's response, demonstrating the flow from student query to tutoring explanation and showcasing the functionality of the TutorService.

This output illustrates a successful tutoring interaction where a new session is created, and the AI responds to the student's economics question with a comprehensive explanation. The tutor's response demonstrates the system's ability to provide relevant, structured, and educational content, showcasing how the DeepSeek model can be effectively used for personalized academic support.

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!