Creating the LLM Manager

Welcome to the lesson on creating the LLM Manager, a crucial component of the AI Cooking Helper project. In previous lessons, you learned about the prompts module and how to make basic LLM calls. Now, we will focus on the LLM Manager, which facilitates interactions with language models like OpenAI's GPT. This manager is responsible for rendering prompts, sending them to the language model, and handling the responses. By the end of this lesson, you will understand how to set up and use the LLM Manager effectively.

To interact with OpenAI's language models, we need to set up an OpenAI client. This client requires an API key and a base URL, which are typically stored in environment variables for security reasons. Let's start by initializing the client.

import os
from openai import OpenAI
from .prompts import render_prompt_from_file

# Initialize OpenAI client (API key read from environment variable)
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"), base_url=os.getenv("OPENAI_BASE_URL"))

In this code snippet:

• We import the os module to access environment variables.
• We import the OpenAI class from the openai package.
• We initialize the client by reading the API key and base URL from environment variables using os.getenv(). This approach keeps sensitive information secure and separate from your code.

The generate_response function is central to the LLM Manager. It renders system and user prompts, sends them to the language model, and returns the response. Let's break it down step-by-step.

First, we need to render the system and user prompts using the render_prompt_from_file function, which was covered in a previous lesson.

system_prompt = render_prompt_from_file("system_prompt", variables)
user_prompt = render_prompt_from_file("user_prompt", variables)

• system_prompt and user_prompt are generated by calling render_prompt_from_file with the respective prompt names and variables. This function replaces placeholders in the prompt templates with actual values.

Next, we send the rendered prompts to the language model using the client.

completion = client.chat.completions.create(
    model=model,
    messages=[
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": user_prompt},
    ],
    temperature=temperature
)

• We use the client.chat.completions.create method to send the prompts.
• The model parameter specifies which language model to use, such as "gpt-4o."
• The messages parameter contains the system and user prompts.
• The temperature parameter controls the randomness of the response. A higher temperature results in more creative responses.

Finally, we extract and return the response from the language model.

return completion.choices[0].message.content.strip()

• We access the first choice in the completion object and retrieve the message content.
• The strip() method removes any leading or trailing whitespace from the response.