ChatGPT

ChatGPT is a neural network model that can generate natural language responses for conversational agents. ChatGPT is based on the GPT-3 architecture a large-scale language model that can generate coherent and diverse texts on various topics, which uses a large-scale neural network to learn from a massive amount of text data. ChatGPT can be used for various applications, such as chatbots, content creation, summarization, and more. ChatGPT is trained on a large corpus of dialogues from Reddit, which provides a rich source of informal and engaging conversations. ChatGPT can be fine-tuned on specific domains or tasks, such as customer service, booking, or trivia. ChatGPT can also be controlled by using special tokens or prefixes to guide the generation process. For example, one can use emoticons, emojis, or keywords to specify the tone, style, or topic of the response. ChatGPT is a powerful and flexible tool for creating conversational agents that can interact with humans in natural and interesting ways.

In this blog post, I will show you how to use ChatGPT to create your own chatbot that can converse with you on any topic. You will need a few things to get started:

- A Google account and access to Google Colab

- A free OpenAI API key

- A basic knowledge of Python

The first step is to open Google Colab and create a new notebook. Google Colab is a cloud-based platform that allows you to run Python code in your browser without installing anything on your computer. You can also use Colab's GPU and TPU resources for free.

The second step is to install the OpenAI library and authenticate with your API key. The OpenAI library is a Python wrapper for the OpenAI API, which lets you access ChatGPT and other models easily. To install the library, run the following code in a Colab cell:

!pip install openai

To authenticate with your API key, run the following code in another cell:

import os

os.environ["OPENAI_API_KEY"] = "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxx"

Replace the x's with your own API key, which you can get from https://beta.openai.com/.

The third step is to define a function that will generate a response from ChatGPT given a prompt. The function will use the openai.Completion endpoint, which takes a text input and returns a text output. The function will also take some parameters that will control the behavior of ChatGPT, such as:

- engine: the name of the model to use. We will use "davinci", which is the most powerful and versatile model available.

- max_tokens: the maximum number of tokens (words) to generate. We will use 150 as a reasonable limit.

- temperature: a value between 0 and 1 that controls the randomness of the generation. A higher temperature means more creativity and diversity, while a lower temperature means more consistency and coherence. We will use 0.9 as a good balance.

- top_p: a value between 0 and 1 that controls the probability of sampling from the most likely tokens. A higher top_p means more diversity, while a lower top_p means more predictability. We will use 0.9 as a good balance.

- frequency_penalty: a value between 0 and 1 that penalizes repeated words or phrases. A higher frequency_penalty means less repetition, while a lower frequency_penalty means more repetition. We will use 0.6 as a good balance.

- presence_penalty: a value between 0 and 1 that penalizes new words or phrases that are not present in the prompt. A higher presence_penalty means more relevance to the prompt, while a lower presence_penalty means more novelty. We will use 0.6 as a good balance.

- stop: a list of strings that indicate when to stop generating. We will use ["\n"] as a simple way to stop at the end of a sentence.

The function will look like this:

import openai

def chat(prompt):

  response = openai.Completion.create(

    engine="davinci",

    prompt=prompt,

    max_tokens=150,

    temperature=0.9,

    top_p=0.9,

    frequency_penalty=0.6,

    presence_penalty=0.6,

    stop=["\n"]

  )

  return response["choices"][0]["text"]