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OpenAI Python Library: The Latest Advancements

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The OpenAI Python library has recently launched its version 0.28.0, bringing with it a range of exciting upgrades. Designed to provide seamless access to the OpenAI API, this library ensures compatibility with various versions of the OpenAI API and offers a set of pre-defined classes for API resources. This article will delve into the key features of the OpenAI Python library and explore its installation and usage.


To begin using the OpenAI Python library, you can simply run the following command:

pip install -upgrade openai

If you wish to install directly from source, you can use the command:

python setup.py install

Optional Dependencies

In addition to the core installation, you may choose to install optional dependencies based on your requirements. For example, if you need support for Weights & Biases, you can use the command:

pip install openai[wandb]

Similarly, data libraries like numpy and pandas are not installed by default due to their size. However, if you encounter a MissingDependencyError, you can install them with the following command:

pip install openai[datalib]


To use the OpenAI Python library, you need to configure it with your account’s secret key. This key is available on the OpenAI website. There are two ways to set the key:

  1. Set it as the OPENAI_API_KEY environment variable before using the library:
export OPENAI_API_KEY='sk-...'
  1. Set openai.api_key to the secret key value in your Python code:
import openai openai.api_key = "sk-..."

Once configured, you can take advantage of the library’s powerful features. For instance, you can list the available models with the following code:

models = openai.Model.list() print(models.data[0].id)

You can also create chat completions using the chat model, as shown in this code snippet:

chat_completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hello world"}]) print(chat_completion.choices[0].message.content)


All endpoints in the OpenAI Python library support a .create method that includes a request_timeout parameter. This parameter allows you to set a time limit for the request in seconds. If the request exceeds this time limit, an openai.error.Timeout error will be raised. For more details on timeouts, refer to the documentation on requests.

Microsoft Azure Endpoints

If you intend to use the library with Microsoft Azure endpoints, you need to provide additional configuration parameters. These parameters include api_type, api_base, api_version, and the deployment name. Here’s an example of how to set these parameters:

import openai openai.api_type = "azure" openai.api_key = "..." openai.api_base = "https://example-endpoint.openai.azure.com" openai.api_version = "2023-05-15"

Please note that currently, Microsoft Azure endpoints support completion, embedding, and fine-tuning operations only. For detailed examples of using Azure endpoints, you can refer to the provided Jupyter notebooks.

Microsoft Azure Active Directory Authentication

To authenticate your Azure endpoint using Microsoft Active Directory, you need to set the api_type to “azure_ad” and pass the acquired credential token to api_key. The other parameters should be set as specified in the previous section. Here’s an example of how to do this:

from azure.identity import DefaultAzureCredential import openai # Request credential default_credential = DefaultAzureCredential() token = default_credential.get_token("https://cognitiveservices.azure.com/.default") # Setup parameters openai.api_type = "azure_ad" openai.api_key = token.token openai.api_base = "https://example-endpoint.openai.azure.com/" openai.api_version = "2023-05-15"

Command-line interface

In addition to the Python library, OpenAI provides a command-line utility that allows easy interaction with the API from your terminal. You can find usage details by running openai api -h. For example, you can list models or create chat completions using this command-line interface.

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Example code

The OpenAI Python library offers a wide range of functionalities, and the OpenAI Cookbook provides numerous examples of how to harness these capabilities. Some of the tasks covered in the cookbook include classification using fine-tuning, clustering, code search, customizing embeddings, question answering from a corpus of documents, recommendations, and visualization of embeddings. If you are looking for practical code examples, the OpenAI Cookbook is an excellent resource.

Furthermore, the Python library also supports conversational models such as gpt-3.5-turbo and text models like babbage-002 or davinci-002. You can utilize the library to create chat completions or generate completions based on a prompt.


The OpenAI Python library version 0.28.0 offers a multitude of features and enhancements, making it a powerful tool for developers. With easy installation, comprehensive usage examples, and optional dependencies for added functionality, this library is a valuable asset for anyone working with the OpenAI API. To get started with the OpenAI Python library and explore its vast potential, visit Ratingperson for more information.

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