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.

To start, ensure you have Python 3.7.1 or newer. If you just want to use the package, run:

pip install --upgrade openai

After you have installed the package, import it at the top of a file:

import openai

To install this package from source to make modifications to it, run the following command from the root of the repository:

python setup.py install

Install dependencies for openai.embeddings_utils:

pip install openai[embeddings]

Install support for Weights & Biases:

pip install openai[wandb]

Data libraries like numpy and pandas 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 MissingDependencyError, 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 OPENAI_API_KEY environment variable before using the library:

export OPENAI_API_KEY='sk-...'

Or set openai.api_key to its value:

openai.api_key = "sk-..."

Examples of how to use this 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.

Most endpoints support a request_timeout param. This param takes a Union[float, Tuple[float, float]] and will raise an openai.error.Timeout error if the request exceeds that time in seconds (See: https://requests.readthedocs.io/en/latest/user/quickstart/#timeouts).

Chat models such as gpt-3.5-turbo and gpt-4 can be called using the chat completions endpoint.

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

You can learn more in our chat completions guide.

Text models such as babbage-002 or davinci-002 (and our legacy completions models) can be called using the completions endpoint.

completion = openai.Completion.create(model="davinci-002", prompt="Hello world") print(completion.choices[0].text)

You can learn more in our completions guide.

Embeddings are designed to measure the similarity or relevance between text strings. To get an embedding for a text string, you can use following:

text_string = "sample text" model_id = "text-embedding-ada-002" embedding = openai.Embedding.create(input=text_string, model=model_id)['data'][0]['embedding']

You can learn more in our embeddings guide.

Fine-tuning a model on training data can both improve the results (by giving the model more examples to learn from) and lower the cost/latency of API calls by reducing the need to include training examples in prompts.

# Create a fine-tuning job with an already uploaded file openai.FineTuningJob.create(training_file="file-abc123", model="gpt-3.5-turbo") # List 10 fine-tuning jobs openai.FineTuningJob.list(limit=10) # Retrieve the state of a fine-tune openai.FineTuningJob.retrieve("ft-abc123") # Cancel a job openai.FineTuningJob.cancel("ft-abc123") # List up to 10 events from a fine-tuning job openai.FineTuningJob.list_events(id="ft-abc123", limit=10) # Delete a fine-tuned model (must be an owner of the org the model was created in) openai.Model.delete("ft:gpt-3.5-turbo:acemeco:suffix:abc123")

You can learn more in our fine-tuning guide.

To log the training results from fine-tuning to Weights & Biases use:

openai wandb sync 

For more information, read the wandb documentation on Weights & Biases.

OpenAI provides a free Moderation endpoint that can be used to check whether content complies with the OpenAI content policy.

moderation_resp = openai.Moderation.create(input="Here is some perfectly innocuous text that follows all OpenAI content policies.")

You can learn more in our moderation guide.

DALL·E is a generative image model that can create new images based on a prompt.

image_resp = openai.Image.create(prompt="two dogs playing chess, oil painting", n=4, size="512x512")

You can learn more in our image generation guide.

The speech to text API provides two endpoints, transcriptions and translations, based on our state-of-the-art open source large-v2 Whisper model.

f = open("path/to/file.mp3", "rb") transcript = openai.Audio.transcribe("whisper-1", f) transcript = openai.Audio.translate("whisper-1", f)

You can learn more in our speech to text guide.

Async support is available in the API by prepending a to a network-bound method:

async def create_chat_completion(): chat_completion_resp = await openai.ChatCompletion.acreate(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hello world"}])

To make async requests more efficient, you can pass in your own aiohttp.ClientSession, but you must manually close the client session at the end of your program/event loop:

from aiohttp import ClientSession openai.aiosession.set(ClientSession()) # At the end of your program, close the http session await openai.aiosession.get().close()

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

# list models openai api models.list # create a chat completion (gpt-3.5-turbo, gpt-4, etc.) openai api chat_completions.create -m gpt-3.5-turbo -g user "Hello world" # create a completion (text-davinci-003, text-davinci-002, ada, babbage, curie, davinci, etc.) openai api completions.create -m ada -p "Hello world" # generate images via DALL·E API openai api image.create -p "two dogs playing chess, cartoon" -n 1 # using openai through a proxy openai --proxy=http://proxy.com api models.list

In order to use the library with Microsoft Azure endpoints, you need to set the api_type, api_base and api_version in addition to the api_key. The api_type 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 = "2023-05-15" # create a chat completion chat_completion = openai.ChatCompletion.create(deployment_id="deployment-name", model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hello world"}]) # print the completion print(chat_completion.choices[0].message.content)

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 chat
• Using Azure embeddings

In order to use Microsoft Active Directory to authenticate to your Azure endpoint, you need to set the api_type to "azure_ad" and pass the acquired credential token to api_key. 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 = "2023-05-15"

This library is forked from the Stripe Python Library.