To demonstrate a simple example of how a Large Language Model (LLM) works using Python, use OpenAI’s GPT-3 model via the OpenAI API. I will provide a Python script that sends a prompt to the model and then receives and displays the generated response.

To get started, you need to have an API key from OpenAI. If you do not have one, you can sign up on the OpenAI website and obtain an API key.

Requirements

  • Python: Make sure you have Python installed (version 3.6 or higher).
  • OpenAI API Key: Sign up on the OpenAI platform and obtain an API key.
  • OpenAI Python Library: Install the OpenAI Python package using pip.
$ pip install openai

Python Code

Below is a Python script that uses the OpenAI GPT-3 model to generate text based on a given prompt:

from openai import OpenAI
import os
from dotenv import load_dotenv
load_dotenv()

def main():
    messages = build_message()
    chat = get_chatgpt_response(messages)

    print(chat.choices[0].message.content)

def build_message():
    question = input("What do you want to ask ChatGPT? ")
    messages = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": question}
    ]

    return messages

def get_chatgpt_response(msgs):
    client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
    chat = client.chat.completions.create(model="gpt-3.5-turbo", messages=msgs)
    return chat

main()

Explanation of the Code

The main() function orchestrates the execution of the script. It performs the following tasks:

  • Calls build_message() to construct a list of messages for the conversation.
  • Calls get_chatgpt_response(messages) to get a response from the ChatGPT model using the constructed messages.
  • Prints the content of the response generated by ChatGPT.

The build_message() function collects a question from the user via standard input (input() function). It creates a list called messages that includes:

  • A system message that sets the behavior of the assistant (“You are a helpful assistant.”).
  • A user message containing the question entered by the user. This list is returned and used in subsequent steps to interact with the ChatGPT model.

get_chatgpt_response(msgs) sends the constructed messages to the OpenAI API and retrieves the response.

  • OpenAI(api_key=os.getenv("OPENAI_API_KEY")): This initializes the OpenAI client using the API key stored in the environment variable OPENAI_API_KEY.
  • client.chat.completions.create(...): This method sends the request to OpenAI’s chat endpoint with the specified model (gpt-3.5-turbo) and the messages list (msgs). It returns a response object.

The response object (chat) is returned, which contains the generated message from the model.

Running the Script

  • Save the script to a file, for example, llm_example.py.
  • Use a .env file and set ‘OPENAI_API_KEY’ with your actual OpenAI API key.
  • Run the script using Python:
$ python llm_example.py

What do you want to ask ChatGPT? What is a Large Language Model (LLM)?

A Large Language Model (LLM) is a type of artificial intelligence (AI) model designed to process and generate human language. These models are trained on massive amounts of text data to understand and generate human-like text in response to input prompts. LLMs have advanced natural language processing capabilities and are used in various applications such as text generation, language translation, and intelligent chatbots. Examples of popular LLMs include OpenAI's GPT (Generative Pre-trained Transformer) series and Google's BERT (Bidirectional Encoder Representations from Transformers).

Conclusion

This simple example demonstrates how to interact with an LLM using Python and the OpenAI API. By sending prompts to the model and receiving text-based responses, you can build applications that leverage the power of LLMs for tasks such as content generation, customer support, language translation, and more. As LLM technology continues to evolve, the potential use cases and applications will only grow, making it a valuable tool in various fields.