" return f\"An error occurred while processing your question: {str(e)}\"\n",
" \n",
"\n",
"### Explanation:\n",
"Let's break down the provided code line by line and explain what each part does in a simple way. The code appears to define a class called `TechnicalQuestionSolver`, which is likely intended to assist with answering technical questions by utilizing the OpenAI API.\n",
"\n",
"### Code Breakdown\n",
"\n",
"1. **Headers Definition**:\n",
" ```python\n",
" headers = {\n",
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n",
" }\n",
" ```\n",
" - This line defines a dictionary named `headers` that includes a `User-Agent` key. \n",
" - The `User-Agent` is typically sent in HTTP requests to identify the client software making the request. It helps web servers understand what type of device and browser is sending the request. In this case, it's simulating a request from a modern web browser running on a Windows operating system.\n",
"\n",
"2. **Class Definition**:\n",
" ```python\n",
" class TechnicalQuestionSolver:\n",
" ```\n",
" - This line defines a new class called `TechnicalQuestionSolver`. Classes are blueprints for creating objects in object-oriented programming, allowing us to bundle data (attributes) and functions (methods) that operate on that data.\n",
"\n",
"3. **Constructor Method**:\n",
" ```python\n",
" def __init__(self, model=\"gpt-4\"): # Fixed the initialization method syntax\n",
" - `__init__` is the constructor method that is automatically called when an instance of the class is created. It initializes the class with the following:\n",
" - `model`: This parameter allows the user to specify which model of OpenAI’s language model to use, defaulting to `\"gpt-4\"`.\n",
" - `self.model`: This sets the instance variable `model` to the provided value, so it can be accessed later by other methods of the class.\n",
" - `self.client`: This creates an instance of `openai.OpenAI()`. This line assumes that there is an OpenAI library imported earlier that provides the functionality to interact with their models.\n",
" - This defines a method named `ask_question` that takes a single parameter `question` of type string and returns a string. This method is responsible for sending the question to the AI and obtaining an explanation.\n",
"\n",
"5. **Try-Except Block**:\n",
" ```python\n",
" try:\n",
" ```\n",
" - The `try` block is used to catch any exceptions (errors) that might occur while running the code inside it. If an error occurs, it prevents the program from crashing and allows it to gracefully handle the problem.\n",
"\n",
"6. **Creating the Prompt**:\n",
" ```python\n",
" prompt = f\"Explain the following technical question in detail:\\n\\n{question}\"\n",
" ```\n",
" - This line creates a string called `prompt` using an f-string (formatted string). It adds a specific text asking the AI to explain the question. The `\\n\\n` adds line breaks for better formatting, placing the actual question right after the initial instruction.\n",
" - Here, after receiving the API response, it extracts the actual content of the response. The structure `response.choices[0].message.content` indicates that:\n",
" - `response.choices` is a list of possible replies returned by the model.\n",
" - `[0]` accesses the first choice (if there are multiple).\n",
" - `.message.content` gets the main text of that choice which is the explanation generated by the AI.\n",
" - The method then returns the explanation to wherever `ask_question` was called from.\n",
"\n",
"9. **Handling Errors**:\n",
" ```python\n",
" except Exception as e:\n",
" return f\"An error occurred while processing your question: {str(e)}\"\n",
" ```\n",
" - If any error occurs during the execution of the `try` block, this `except` block will catch it. It creates an error message that includes the original error converted to a string. It then returns this error message, making it clear that something went wrong.\n",
"\n",
"### Summary\n",
"\n",
"The `TechnicalQuestionSolver` class is designed to facilitate query processing through OpenAI's model. It initializes an OpenAI client object, builds a prompt based on a user question, sends that to the API, and retrieves a detailed explanation of the technical question, all while managing potential errors gracefully. This modular approach allows for easy use and extension of the functionality."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gpt_helper.display_answer(question) # Using GPT\n"