Building the Tutor Service Layer for a Personal Tutor Application

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:

import uuid
from openai import OpenAI
from models.session import SessionManager

class TutorService:
    def __init__(self):
        self.session_manager = SessionManager()
        self.deepseek_client = OpenAI()
        self.system_prompt = self.load_system_prompt('data/system_prompt.txt')

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 same load_system_prompt method we implemented in Unit 1.

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. Let's quickly recap the load_system_prompt method we implemented earlier, which loads the prompt from a file:

def load_system_prompt(self, file_path: str) -> str:
    """Load the tutor system prompt from file."""
    try:
        with open(file_path, 'r') as f:
            return f.read()
    except Exception as e:
        print(f"Error loading system prompt: {e}")
        return "You are a helpful tutor."