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 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:
import openai
openai.api_key = "sk-..."
# list models
models = openai.Model.list()
# print the first model's id
print(models.data[0].id)
# create a chat completion
chat_completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hello world"}])
# print the chat completion
print(chat_completion.choices[0].message.content)
All endpoints have a .create
method that supports 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).
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(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:
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 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=https://proxy.com api models.list
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.
Conversational models such as gpt-3.5-turbo
can be called using the chat completions endpoint.
import openai
openai.api_key = "sk-..." # supply your API key however you choose
completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hello world"}])
print(completion.choices[0].message.content)
Text models such as text-davinci-003
, text-davinci-002
and earlier (ada
, babbage
, curie
, davinci
, etc.) can be called using the completions endpoint.
import openai
openai.api_key = "sk-..." # supply your API key however you choose
completion = openai.Completion.create(model="text-davinci-003", prompt="Hello world")
print(completion.choices[0].text)
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, model=model_id)['data'][0]['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 classification using embeddings
- Recommendation using embeddings
For more information on embeddings and the types of embeddings OpenAI offers, read the embeddings guide in the OpenAI documentation.
Fine-tuning a model on training data can both improve the results (by giving the model more examples to learn from) and reduce the cost/latency of API calls (chiefly through reducing the need to include training examples in prompts).
Examples of fine-tuning are shared in the following Jupyter notebooks:
- Classification with fine-tuning (a simple notebook that shows the steps required for fine-tuning)
- Fine-tuning a model that answers questions about the 2020 Olympics
Sync your fine-tunes to Weights & Biases to track experiments, models, and datasets in your central dashboard with:
openai wandb sync
For more information on fine-tuning, read the fine-tuning guide in the OpenAI documentation.
OpenAI provides a Moderation endpoint that can be used to check whether content complies with the OpenAI content policy
import openai
openai.api_key = "sk-..." # supply your API key however you choose
moderation_resp = openai.Moderation.create(input="Here is some perfectly innocuous text that follows all OpenAI content policies.")
See the moderation guide for more details.
import openai
openai.api_key = "sk-..." # supply your API key however you choose
image_resp = openai.Image.create(prompt="two dogs playing chess, oil painting", n=4, size="512x512")
import openai
openai.api_key = "sk-..." # supply your API key however you choose
f = open("path/to/file.mp3", "rb")
transcript = openai.Audio.transcribe("whisper-1", f)
Async support is available in the API by prepending a
to a network-bound method:
import openai
openai.api_key = "sk-..." # supply your API key however you choose
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:
import openai
from aiohttp import ClientSession
openai.aiosession.set(ClientSession())
# At the end of your program, close the http session
await openai.aiosession.get().close()
See the usage guide for more details.
- Python 3.7.1+
In general, we want to support the versions of Python that our customers are using. If you run into problems with any version issues, please let us know on our support page.
This library is forked from the Stripe Python Library.