How to Use OpenAI API in Python Read it later

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Are you tired of writing code that doesn’t generate the results you want? Are you looking for a way to improve your code’s natural language processing capabilities? Look no further than the OpenAI API! In this blog, we will discuss how to use the OpenAI API in Python to enhance your code’s language understanding abilities.

But before we dive in, let’s take a look at what is OpenAI and what its API can do for you.

What is OpenAI?

OpenAI is an artificial intelligence research laboratory consisting of the for-profit corporation OpenAI LP and its parent company, the non-profit OpenAI Inc.

The organization was founded in 2015 by a group of tech industry leaders, including Elon Musk, Sam Altman, Greg Brockman, and Ilya Sutskever. The goal of OpenAI is to advance artificial intelligence in a way that benefits humanity as a whole.

OpenAI’s research focuses on developing and improving technologies such as natural language processing, robotics, and machine learning. The organization’s mission is to create safe, beneficial AI systems that can help solve some of the world’s most pressing problems.

They have made significant contributions to the field of artificial intelligence. Some of their most notable projects include GPT-3, a language model capable of generating human-like text, and DALL-E, an AI system capable of generating original images from textual descriptions.

Let’s now explore what the OpenAI API is and gain a better understanding of its functionalities.

What is the OpenAI API?

The OpenAI API is a powerful tool that provides natural language processing (NLP) capabilities to your code. It is a cloud-based service that allows you to access state-of-the-art (SOTA) language models that can analyze and understand text data.

The OpenAI API offers a variety of language models, each with its own strengths and weaknesses.

Let’s now see what you can really do with OpenAI API.

What Can the OpenAI API Do?

Some of the magical things that the OpenAI’s API can do for you:

Text Generation Using GPT

GPT-4 (Generative Pre-trained Transformer 4) is one of the most powerful language models developed by OpenAI. It is a deep learning model that uses unsupervised learning to generate human-like text.

The model has been trained with 10 trillion parameters, and it is currently the largest language model available, capable of completing complex tasks such as:

  • Text Completion: OpenAI API can generate text completions for a given prompt. This can be useful in a wide range of applications, from chatbots to text editors.
  • Text Classification: The OpenAI API can classify text into categories such as sentiment, topic, and intent. This can be useful in applications such as spam filtering, content moderation, and customer support.
  • Language Translation: The OpenAI API can translate text from one language to another. This can be useful in applications such as international communication, document translation, and language learning.
  • Question Answering: The OpenAI API can answer questions based on a given context. This can be useful in applications such as virtual assistants, customer support, and educational resources.
  • Text Summarization: The OpenAI API can summarize long blocks of text into a shorter summary. This can be useful in applications such as news articles, research papers, and legal documents.
  • Language Generation: The OpenAI API can generate coherent paragraphs of text based on a given prompt. This can be useful in applications such as creative writing, chatbots, and virtual assistants.

Image Generation Using DALL-E

The OpenAI API can also generate an image based on the description you provide to the model. This is accomplished using the DALL-E (pronounced like “dal-eye”) model, which is a neural network that can generate high-quality images from textual descriptions.

DALL-E is a language model developed by OpenAI that generates images from textual descriptions. It is named after the artist Salvador Dali and the character WALL-E from the Pixar movie.

DALL-E can generate a wide range of images, including objects, scenes, and creatures, from textual descriptions, making it a powerful tool for creative applications.

Understanding Images Using CLIP

CLIP (Contrastive Language-Image Pre-Training) is a language model developed by OpenAI that can understand both textual and visual information.

It is trained on a large dataset of images and their corresponding captions, allowing it to recognize objects, scenes, and concepts in images based on their textual descriptions.

This makes CLIP a valuable tool for image classification, object detection, shape detection, and other computer vision tasks.

Speech to Text Conversion Using Whisper

The OpenAI API isn’t just limited to natural language processing with text. It also offers a unique speech recognition tool called Whisper.

Whisper is an end-to-end deep learning-based speech recognition system that can transcribe audio files into text.

Programming Using Codex

Codex is a language model developed by OpenAI specifically for programming tasks. It can understand natural language descriptions of code and generate corresponding code snippets.

Codex has been trained on a large dataset of code and can generate code in a variety of programming languages, including Python, JavaScript, Ruby, and Go.

Now that you understant the OpenAI Models and API, let’s get started with how to use it in Python.

Setting up the OpenAI API in Python

Before you can use the OpenAI API in your Python projects, you need to set up your OpenAI account, install the OpenAI Python package, and authenticate your API key.

  1. Sign up for an OpenAI account

To sign up for an OpenAI account, go to the OpenAI website and click on the “Sign up” button. You will be prompted to enter your email address and create a password.

Once you’ve entered your information, click on the “Create Account” button.

  1. Create an API key

Once you’ve created your OpenAI account, you need to create an API key. To create an API key, go to the “API” tab on the OpenAI website and click on the “Create API Key” button.

You will be prompted to name your API key and choose which permissions it should have. Once you’ve created your API key, copy it to your clipboard.

  1. Install the OpenAI Python package

To install the OpenAI Python package, you can use pip, the Python package manager. Open up your command prompt or terminal and run the following command:

pip install openai

This will install the OpenAI Python package and all of its dependencies in your system.

  1. Authenticate your API key

To authenticate your OpenAI API key, you need to set it as the default API key for the OpenAI Python package.

You can do this by running the following command in your Python script:

import openai
openai.api_key = "YOUR_API_KEY"

Replace “YOUR_API_KEY” with the API key that you copied to your clipboard in step 2.

Setting up your OpenAI API key

To keep your API key secure, you should avoid hard-coding it into your code. Instead consider either:

  • Storing OpenAI API Key as an environment variable, or
  • Using the openai_secret_manager library to securely manage the API key

Using Environment Variable

To set up your API key using an environment variable, follow these steps:

  1. Open a terminal or command prompt window.
  2. Type the following command: export OPENAI_API_KEY=<your-api-key>
  3. Replace <your-api-key> with your actual API key and execute the command.

Your API key is now stored as an environment variable on your system.

Easy right? Let’s now verify if our API key has been set up correctly or not.

To access your API key in your code, you can use the following code:

import os
api_key = os.environ["OPENAI_API_KEY"]

This code retrieves the value of the OPENAI_API_KEY environment variable and stores it in a variable called api_key. Now you can use the api_key variable to authenticate your OpenAI API requests.

Using OpenAI Secret Manager

The openai_secret_manager library provides a secure way to manage your API key.

To use this library, follow these steps:

  1. Install the openai_secret_manager library by running the following command in your terminal or command prompt using the command: pip install openai_secret_manager
  2. Import the openai_secret_manager library in your code using the following code: from openai_secret_manager import get_secret
  3. Retrieve your API key using the get_secret() function provided by the openai_secret_manager library.

You can do this by adding the following code to your project:

import openai_secret_manager

assert "openai" in openai_secret_manager.get_services()
secrets = openai_secret_manager.get_secret("openai")

openai.api_key = secrets["api_key"]

With these steps completed, you’re ready to start using the OpenAI API in your Python projects.

Let’s now learn how to send requests to the API and handle the responses that we will receive.

Sending Requests to OpenAI API in Python

To send a request to the OpenAI API using Python, you need to create a request object and set the appropriate parameters.

The openai package provides a convenient Completion.create() method for creating a request object.


import openai

openai.api_key = "YOUR_API_KEY"

prompt = "Hello, my name is"
response = openai.Completion.create(

OpenAI Completion Create Parameters

  • engine parameter specifies which language model to use for the completion.
  • prompt parameter specifies the prompt to be completed.
  • max_tokens parameter specifies the maximum number of tokens in the completion.
  • n parameter specifies the number of completions to generate.
  • stop parameter specifies the stopping sequence for the completion.
  • temperature parameter controls the diversity of the generated completions.

Handling OpenAI API Responses in Python

When you send a request to the OpenAI API, you will receive a response object.

The response object contains the generated completions as well as other metadata.

Here’s how you can access the generated completions:

completion_text = response.choices[0].text

In this example, we are accessing the generated completion by getting the text attribute of the first element in the choices list of the response object.

If you specified more than one completion to be generated, you can access the other completions by iterating over the choices list.

The response object also contains other metadata such as the time taken to generate the completion, the ID of the request, and the ID of the language model used. You can access this metadata using the appropriate attributes of the response object.

Using the ChatGPT API in Python

Now that you have the proper understanding of how to send and handle OpenAI API responses, you can start using the ChatGPT API in your Python code.

The basic workflow for using the API is as follows:

  1. Create an OpenAI API client object
  2. Call the openai.Completion.create() method to generate natural language responses
  3. Process the response

Here’s an example code snippet that demonstrates this workflow:

import openai
openai.api_key = "your-api-key" # or use the method we defined earlier

def generate_response(prompt):
    response = openai.Completion.create(
    return response.choices[0].text.strip()

prompt = "What is the capital of India?"
response = generate_response(prompt)

Using Codex to convert natural language to code

There are two ways of using Codex using OpenAI Python library:

  • Using OpenAI Completion function with engine set to davinci-codex
  • Using OpenAI Code function

Let’s learn about both of them.

Here’s an example of how to use Codex to generate Python code from natural language using engine set to davinci-codex:

import openai
import json

# Authenticate with the API key
openai.api_key = "YOUR_API_KEY"

# Set the engine to davinci-codex
engine = "davinci-codex"

# Define the natural language prompt
prompt = (
    "Write a Python function to calculate the factorial of a number."

# Set up the parameters for the completion
parameters = {
    "prompt": prompt,
    "max_tokens": 100,
    "n": 1,
    "stop": "\n",
    "temperature": 0.5,
    "frequency_penalty": 0,
    "presence_penalty": 0,

# Generate the code completion
response = openai.Completion.create(engine=engine, **parameters)

# Extract the code from the response
code = response.choices[0].text.strip()

# Print the generated code

Using Code Method in OpenAI Python library:

import openai

openai.api_key = "your_api_key"

response = openai.Code.create(
    prompt='Write a Python function to calculate the factorial of a number.',

Codex can be used to generate code for a wide variety of programming languages, including Python, JavaScript, Ruby, and many more.

It can also be used to generate code snippets for specific tasks or to complete partial code examples.

Using DALL-E to Generate Image

Here’s an example of how to generate an image using DALL-E OpenAI Python library:

response = openai.Image.create(
    prompt="A blue bird sitting on a branch",

image_url = response["data"][0]["url"]

You can then use the Pillow Python library to download and display the generated image:

from PIL import Image
import requests
from io import BytesIO

response = requests.get(image_url)
img =

Wrap Up

Congratulations, you’ve learned how to use the OpenAI API in Python! In this blog, we discussed what the OpenAI API is, how to set up an account, how to install the OpenAI Python package, how to authenticate your API key, and how to use the API in your projects. We also provided an example of how to use the API to complete a sentence in your code.

Remember, the OpenAI API is a powerful tool that can help you improve your code’s natural language processing capabilities. With the right skills and creativity, you can use the OpenAI API to build amazing projects that can understand and analyze natural language data.

So, what are you waiting for? Start using the OpenAI API in your projects today and see how it can transform the way you write code!


  1. OpenAI official website:
  2. OpenAI API documentation:
  3. OpenAI GitHub repository:
  4. OpenAI Python library documentation:
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