openai／openai-python

The OpenAI Python library provides convenient access to the OpenAI API from applications written in the Python language. It includes a pre-defined set of classes for API resources that initialize themselves dynamically from API responses which makes it compatible with a wide range of versions of the OpenAI API.

You can find usage examples for the OpenAI Python library in our API reference and the OpenAI Cookbook.

You don't need this source code unless you want to modify the package. If you just want to use the package, just run:

pip install --upgrade openai

Install from source with:

python setup.py install

Install dependencies for :

pip install openai[embeddings]

Install support for Weights & Biases:

Data libraries like and are not installed by default due to their size. They’re needed for some functionality of this library, but generally not for talking to the API. If you encounter a , install them with:

pip install openai[datalib]

The library needs to be configured with your account's secret key which is available on the website. Either set it as the environment variable before using the library:

export OPENAI_API_KEY='sk-...'

Or set to its value:

import openai openai.api_key = "sk-..."  # list engines engines = openai.Engine.list()  # print the first engine's id print(engines.data[].id)  # create a completion completion = openai.Completion.create(engine="ada", prompt="Hello world")  # print the completion print(completion.choices[].text)

All endpoints have a method that supports a param. This param takes a and will raise an error if the request exceeds that time in seconds (See: ).

In order to use the library with Microsoft Azure endpoints, you need to set the , and in addition to the . The must be set to 'azure' and the others correspond to the properties of your endpoint. In addition, the deployment name must be passed as the engine parameter.

import openai openai.api_type = "azure" openai.api_key = "..." openai.api_base = "https://example-endpoint.openai.azure.com" openai.api_version = "2022-12-01"  # create a completion completion = openai.Completion.create(engine="deployment-name", prompt="Hello world")  # print the completion print(completion.choices[].text)

Please note that for the moment, the Microsoft Azure endpoints can only be used for completion, embedding, and fine-tuning operations. For a detailed example of how to use fine-tuning and other operations using Azure endpoints, please check out the following Jupyter notebooks:

• Using Azure completions
• Using Azure fine-tuning
• Using Azure embeddings

In order to use Microsoft Active Directory to authenticate to your Azure endpoint, you need to set the to "azure_ad" and pass the acquired credential token to . The rest of the parameters need to be set as specified in the previous section.

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 = "2022-12-01"  # ...

This library additionally provides an command-line utility which makes it easy to interact with the API from your terminal. Run for usage.

# list engines openai api engines.list  # create a completion openai api completions.create -e ada -p "Hello world"  # generate images via DALL・E API openai api image.create -p "two dogs playing chess, cartoon" -n 1

Examples of how to use this Python library to accomplish various tasks can be found in the OpenAI Cookbook. It contains code examples for:

• Classification using fine-tuning
• Clustering
• Code search
• Customizing embeddings
• Question answering from a corpus of documents
• Recommendations
• Visualization of embeddings
• And more

Prior to July 2022, this OpenAI Python library hosted code examples in its examples folder, but since then all examples have been migrated to the OpenAI Cookbook.

In the OpenAI Python library, an embedding represents a text string as a fixed-length vector of floating point numbers. Embeddings are designed to measure the similarity or relevance between text strings.

To get an embedding for a text string, you can use the embeddings method as follows in Python:

import openai openai.api_key = "sk-..."  # supply your API key however you choose  # choose text to embed text_string = "sample text"  # choose an embedding model_id = "text-similarity-davinci-001"  # compute the embedding of the text embedding = openai.Embedding.create(input=text_string, engine=model_id)['data'][]['embedding']

An example of how to call the embeddings method is shown in this get embeddings notebook.

Examples of how to use embeddings are shared in the following Jupyter notebooks:

• Classification using embeddings
• Clustering using embeddings
• Code search using embeddings
• Semantic text search using embeddings
• User and product embeddings
• Zero-shot classi
  
  

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