openai-python-main

# OpenAI Python Library 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](https://beta.openai.com/docs/api-reference?lang=python) and the [OpenAI Cookbook](https://github.com/openai/openai-cookbook/). ## Installation You don't need this source code unless you want to modify the package. If you just want to use the package, just run: ```sh pip install --upgrade openai ``` Install from source with: ```sh python setup.py install ``` ### Optional dependencies Install dependencies for [`openai.embeddings_utils`](openai/embeddings_utils.py): ```sh pip install openai[embeddings] ``` Install support for [Weights & Biases](https://wandb.me/openai-docs): ``` 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: ```sh pip install openai[datalib] ```` ## Usage The library needs to be configured with your account's secret key which is available on the [website](https://platform.openai.com/account/api-keys). Either set it as the `OPENAI_API_KEY` environment variable before using the library: ```bash export OPENAI_API_KEY='sk-...' ``` Or set `openai.api_key` to its value: ```python 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 completion completion = openai.Completion.create(model="ada", prompt="Hello world") # print the completion print(completion.choices[0].text) ``` ### Params 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). ### Microsoft Azure Endpoints 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. ```python import openai openai.api_type = "azure" openai.api_key = "..." openai.api_base = "https://example-endpoint.openai.azure.com" openai.api_version = "2023-03-15-preview" # create a completion completion = openai.Completion.create(deployment_id="deployment-name", prompt="Hello world") # print the completion print(completion.choices[0].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](https://github.com/openai/openai-cookbook/tree/main/examples/azure/completions.ipynb) * [Using Azure fine-tuning](https://github.com/openai/openai-cookbook/tree/main/examples/azure/finetuning.ipynb) * [Using Azure embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/azure/embeddings.ipynb) ### Microsoft Azure Active Directory Authentication 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. ```python 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 = "2023-03-15-preview" # ... ``` ### Command-line interface 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. ```sh # list models openai api models.list # create a completion openai api completions.create -m ada -p "Hello world" # create a chat completion openai api chat_completions.create -m gpt-3.5-turbo -g user "Hello world" # generate images via DALL·E API openai api image.create -p "two dogs playing chess, cartoon" -n 1 ``` ## Example code Examples of how to use this Python library to accomplish various tasks can be found in the [OpenAI Cookbook](https://github.com/openai/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](https://github.com/openai/openai-cookbook/). ### Chat Conversational models such as `gpt-3.5-turbo` can be called using the chat completions endpoint. ```python 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) ``` ### Embeddings 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: ```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](https://github.com/openai/openai-cookbook/blob/main/examples/Get_embeddings.ipynb). Examples of how to use embeddings are shared in the following Jupyter notebooks: - [Classification using embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/Classification_using_embeddings.ipynb) - [Clustering using embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/Clustering.ipynb) - [Code search using embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/Code_search.ipynb) - [Semantic text search using embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/Semantic_text_search_using_embeddings.ipynb) - [User and product embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/User_and_product_embeddings.ipynb) - [Zero-shot classification using embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/Zero-shot_classification_with_embeddings.ipynb) - [Recommendation using embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/Recommendation_using_embeddings.ipynb) For more information on embeddings and the types of embeddings OpenAI offers, read the [embeddings guide](https://beta.openai.com/docs/guides/embeddings) in the OpenAI documentation. ### Fine-tuning Fine-tuning a model on training data can both improve the results (by giving the model more exa