# README
[](https://opensource.org/license/apache-2-0/)
[](https://hex.pm/packages/openai_ex)
`OpenaiEx` is an Elixir library that provides a community-maintained OpenAI API client.
Portions of this project were developed with assistance from ChatGPT 3.5 and 4, as well as Claude 3 Opus and Claude 3.5 Sonnet. However, every line of code is human curated (by me 😇).
All API endpoints and features (as of May 1, 2024) are supported, including the **Assistants API Beta 2 with Run streaming**, DALL-E-3, Text-To-Speech, the **tools support** in chat completions, and the **streaming version** of the chat completion endpoint. Streaming request **cancellation** is also supported.
Configuration of Finch pools and API base url are supported.
There are some differences compared to other elixir openai wrappers.
* I tried to faithfully mirror the naming/structure of the [official python api](https://github.com/openai/openai-python). For example, content that is already in memory can be uploaded as part of a request, it doesn't have to be read from a file at a local path.
* I was developing for a livebook use-case, so I don't have any config, only environment variables.
* Streaming API versions, with request cancellation, are supported.
* The underlying transport is finch, rather than httpoison
* 3rd Party (including local) LLMs with an OpenAI proxy, as well as the **Azure OpenAI API**, are considered legitimate use cases.
Discussion and announcements are on [this thread in Elixir Forum](https://elixirforum.com/t/openai-ex-openai-api-client-library/)
## Installation and Usage
For installation instructions and detailed usage examples, please look at the [User Guide on hexdocs](https://hexdocs.pm/openai_ex/userguide.html). The guide is a Livebook, and you can run all of the code in it without creating a new project. Practically every API call has a running example in the User Guide.
There are also Livebook examples for
* [Streaming Orderbot](https://hexdocs.pm/openai_ex/streaming_orderbot.html) An example of how to use ChatCompletion streaming in a Chatbot. This is a streaming version of the next Livebook in this list.
* The [Deeplearning.AI Orderbot](https://hexdocs.pm/openai_ex/dlai_orderbot.html). This notebook is an elixir / Kino translation of the python notebook in [Lesson 8](https://learn.deeplearning.ai/chatgpt-prompt-eng/lesson/8/chatbot), of [Deeplearning.AI](https://www.deeplearning.ai/)'s course [ChatGPT Prompt Engineering for Developers](https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/).
* A [Completions Chatbot](https://hexdocs.pm/openai_ex/completions.html) which can be deployed as a Livebook app. The deployed app displays 2 forms, one for normal completions and another for streaming completions.
* An [Image Generation UI](https://hexdocs.pm/openai_ex/images.html)
These are hosted on [hexdocs](https://hexdocs.pm/openai_ex) and can be used as inspiration / starters for your own projects.
## Development
The following section is only for developers that want to contribute to this repository.
This library was developed using a Livebook docker image that runs inside a VS Code devcontainer. The `.devcontainer` folder contains all of the relevant files.
To get started, clone the repository to your local machine and open it in VS Code. Follow the prompts to open it in a container.
After the container is up and running in VS Code, you can access livebook at http://localhost:8080. However, you'll need to enter a password that's stored in the environment variable `LIVEBOOK_PASSWORD`. This variable needs to be defined in the `.devcontainer/.env` file, which is explained below.
### Environment Variables and Secrets
To set environment variables for devcontainer development, you can create a `.env` file in the `.devcontainer` folder. Any secrets, such as `OPENAI_API_KEY` and `LIVEBOOK_PASSWORD`, can be defined in this file as environment variables. Note that this `.env` file should not be included in version control, and it is already included in the .gitignore file for this reason.
You can find a sample `env` file in the same folder, which you can use as a template for your own `.env` file. These variables will be passed to Livebook via `docker-compose.yml`.
