Understanding the “AttributeError” in Python’s OpenAI API

Deciphering Python’s OpenAI API Error: Missing ‘Completion’ Attribute

Diving into a common hurdle faced when utilizing the OpenAI API with Python, encountering an error message indicating that the module ‘openai’ does not possess the attribute ‘Completion’. Could it be searching for ‘completions’ instead? Let’s unravel this together!

What Will You Learn?

In this guide, you will master resolving a specific AttributeError linked to the OpenAI API in Python. We are dedicated to streamlining your coding experience by tackling and rectifying this error.

Introduction to Problem and Solution

Encountering an AttributeError can be bewildering, especially when anticipating smooth code execution. This particular issue surfaces when trying to access a non-existent attribute of a module, such as �Completion� within the �openai� package. The error hints at a probable misunderstanding or typo, suggesting a potential usage of �completions� instead.

To address this challenge effectively, it is vital to align ourselves with the latest documentation and updates from OpenAI. APIs evolve continuously, introducing new features or modifications in their structure; hence, staying informed is crucial. Our strategy involves validating our existing setup against official resources and adjusting our code correspondingly.

Code Solution

import openai

# Ensure proper configuration of your API key
openai.api_key = 'your_api_key_here'

response = openai.Completion.create(
  prompt="Once upon a time...",


# Copyright PHD

Diving Deeper into the Solution

The key to resolving this error lies in recognizing that Completion.create() is indeed the correct method invocation according to recent versions of the openai library (assuming utilization of an updated version). When facing an AttributeError, confirming that your library version supports this method becomes paramount. Transitioning from potentially invoking a non-existent attribute (Completion) to calling Completion.create() aligns us with established practices for generating text completions using OpenAI�s GPT models.

Moreover, setting up your environment accurately (including package installation and API key configuration) significantly aids in preventing such errors. Our example not only showcases how to set up your API key but also illustrates effective method calls within current standards.

FAQ About Using OpenAI’s Python Library

  1. How do I install the openai Python library?

    pip install openai
    # Copyright PHD
  2. Where can I find my OpenAI API key? Your API key is accessible on your account dashboard on the OpenAI website post registration.

  3. Can I use different engines for completions? Yes, engines like “text-davinci-003” offer diverse capabilities; explore them based on your requirements.

  4. What does temperature control in requests? It adjusts randomness: lower values yield more predictable outputs.

  5. How do I limit output length? Utilize max_tokens to specify maximum length; adjust as needed.

  6. Can I fine-tune models for specific tasks? OpenAI provides options for model fine-tuning; refer to their documentation for specifics.


Understanding and overcoming common errors like these demystifies working with advanced APIs such as those provided by OpenAI. It enhances our debugging proficiency and expands our ability to seamlessly integrate external services into our projects. Regularly revisiting official documentation keeps us aligned with best practices and updates�crucial assets in navigating challenges effortlessly.

Leave a Comment