Revolutionize Your Chatbot Game with the Chat GPT API and Python — Get Started in Just 3 Minutes!

Chat GPT is a powerful tool that allows developers to build conversational agents and chatbots using natural language processing (NLP) and machine learning (ML). It uses a variant of the GPT (Generative Pre-trained Transformer) language model to generate responses to user inputs in a conversational context. In this blog, we will learn how to use the Chat GPT API with Python to build a simple chatbot.

Prerequisites:

• An OpenAI API key: You will need an OpenAI key to use the API. You can sign up for a free trial on the OpenAI website. Link to API keys here!
• Python 3: This tutorial is written in Python 3, so you will need to have Python 3 installed on your machine.
• The OpenAI library. You can install it using pip: pip install openai

Setting up the environment:

First, let’s import the necessary libraries and set up the API key.

import openai

# Replace YOUR_API_KEY with your OpenAI API key
openai.api_key = "YOUR_API_KEY"

Sending a request to the API:

Now that we have our API key, we can start making requests to the API. We will use the openai.Completion.create() function with the following parameters:

• model_engine: The name of the model you want to use. We will be using the text-davinci-003 model for this tutorial. This is the most capable GPT-3 model. More info here. (Note that the latest Chat GPT model isn’t available yet via API)
• prompt: The prompt or context for the conversation. This can be a single line of text or a multi-line prompt separated by newlines.
• max_tokens: The maximum number of tokens (words and punctuation) to generate in the response. The minimum is 1 and the maximum is 2048.
• And a few more parameters that you can use to tweak the answer.

Here’s an example of how to ask a question to the API:

# Set the model and prompt
model_engine = "text-davinci-003"
prompt = "Write a blog on ChatGPT"

# Set the maximum number of tokens to generate in the response
max_tokens = 1024

# Generate a response
completion = openai.Completion.create(
    engine=model_engine,
    prompt=prompt,
    max_tokens=max_tokens,
    temperature=0.5,
    top_p=1,
    frequency_penalty=0,
    presence_penalty=0
)

# Print the response
print(completion.choices[0].text)

Response:

Now that we have the response from the API, we can parse it and use the generated response in a chatbot, website,…

PS you don’t have an unlimited amount free credits, think about that when building ;)

❤ ️If you found this article helpful, I’d be grateful if you could follow me on Medium and give it a clap or two. Your support means a lot to me. Thank you!

*Check out how you can build out a LinkedIn Post Generator in my next blog:

Build a Pro-Level LinkedIn Post Generator in Minutes with GPT-3 and Streamlit

Enjoy!
Alexandre t’Kint