Ruby::OpenAI

Use the OpenAI API with Ruby! 🤖❤️

Generate text with GPT-3, create images with DALL·E, or write code with Codex...

Want to instantly make your own AIs without writing a line of code? Check out Peaceterms!

Installation

Bundler

Add this line to your application's Gemfile:

    gem "ruby-openai"

And then execute:

$ bundle install

Gem install

Or install with:

$ gem install ruby-openai

and require with:

    require "ruby/openai"

Usage

Get your API key from https://beta.openai.com/account/api-keys
If you belong to multiple organizations, you can get your Organization ID from https://beta.openai.com/account/org-settings

Quickstart

For a quick test you can pass your token directly to a new client:

    client = OpenAI::Client.new(access_token: "access_token_goes_here")

With Config

For a more robust setup, you can configure the gem with your API keys, for example in an openai.rb initializer file. Never hardcode secrets into your codebase - instead use something like dotenv to pass the keys safely into your environments.

    Ruby::OpenAI.configure do |config|
        config.access_token = ENV.fetch('OPENAI_ACCESS_TOKEN')
        config.organization_id = ENV.fetch('OPENAI_ORGANIZATION_ID') # Optional.
    end

Then you can create a client like this:

    client = OpenAI::Client.new

Models

There are different models that can be used to generate text. For a full list and to retrieve information about a single models:

    client.models.list
    client.models.retrieve(id: "text-ada-001")

Examples

GPT-3
- text-ada-001
- text-babbage-001
- text-curie-001
- text-davinci-001
Codex (private beta)
- code-davinci-002
- code-cushman-001

Completions

Hit the OpenAI API for a completion:

    response = client.completions(
        parameters: {
            model: "text-davinci-001",
            prompt: "Once upon a time",
            max_tokens: 5
        })
    puts response["choices"].map { |c| c["text"] }
    => [", there lived a great"]

Edits

Send a string and some instructions for what to do to the string:

    response = client.edits(
        parameters: {
            model: "text-davinci-edit-001",
            input: "What day of the wek is it?",
            instruction: "Fix the spelling mistakes"
        }
    )
    puts response.dig("choices", 0, "text")
    => What day of the week is it?

Embeddings

You can use the embeddings endpoint to get a vector of numbers representing an input. You can then compare these vectors for different inputs to efficiently check how similar the inputs are.

    client.embeddings(
        parameters: {
            model: "babbage-similarity",
            input: "The food was delicious and the waiter..."
        }
    )

Files

Put your data in a .jsonl file like this:

    {"text": "puppy A is happy", "metadata": "emotional state of puppy A"}
    {"text": "puppy B is sad", "metadata": "emotional state of puppy B"}

and pass the path to client.files.upload to upload it to OpenAI, and then interact with it:

    client.files.upload(parameters: { file: "path/to/puppy.jsonl", purpose: "search" })
    client.files.list
    client.files.retrieve(id: 123)
    client.files.delete(id: 123)

Fine-tunes

Put your fine-tuning data in a .jsonl file like this:

    {"prompt":"Overjoyed with my new phone! ->", "completion":" positive"}
    {"prompt":"@lakers disappoint for a third straight night ->", "completion":" negative"}

and pass the path to client.files.upload to upload it to OpenAI and get its ID:

    response = client.files.upload(parameters: { file: "path/to/sentiment.jsonl", purpose: "fine-tune" })
    file_id = JSON.parse(response.body)["id"]

You can then use this file ID to create a fine-tune model:

    response = client.finetunes.create(
        parameters: {
        training_file: file_id,
        model: "text-ada-001"
    })
    fine_tune_id = JSON.parse(response.body)["id"]

That will give you the fine-tune ID. If you made a mistake you can cancel the fine-tune model before it is processed:

    client.finetunes.cancel(id: fine_tune_id)

You may need to wait a short time for processing to complete. Once processed, you can use list or retrieve to get the name of the fine-tuned model:

    client.finetunes.list
    response = client.finetunes.retrieve(id: fine_tune_id)
    fine_tuned_model = JSON.parse(response.body)["fine_tuned_model"]

This fine-tuned model name can then be used in completions:

    response = client.completions(
        parameters: {
            model: fine_tuned_model,
            prompt: "I love Mondays!"
        }
    )
    JSON.parse(response.body)["choices"].map { |c| c["text"] }

You can delete the fine-tuned model when you are done with it:

    client.finetunes.delete(fine_tuned_model: fine_tuned_model)

Image Generation

Generate an image using DALL·E! The size of any generated images must be one of 256x256, 512x512 or 1024x1024 - if not specified the image will default to 1024x1024.

    response = client.images.generate(parameters: { prompt: "A baby sea otter cooking pasta wearing a hat of some sort", size: "256x256" })
    puts response.dig("data", 0, "url")
    => "https://oaidalleapiprodscus.blob.core.windows.net/private/org-Rf437IxKhh..."

Image Edit

Fill in the transparent part of an image, or upload a mask with transparent sections to indicate the parts of an image that can be changed according to your prompt...

    response = client.images.edit(parameters: { prompt: "A solid red Ruby on a blue background", image: "image.png", mask: "mask.png" })
    puts response.dig("data", 0, "url")
    => "https://oaidalleapiprodscus.blob.core.windows.net/private/org-Rf437IxKhh..."

Image Variations

Create n variations of an image.

    response = client.images.variations(parameters: { image: "image.png", n: 2 })
    puts response.dig("data", 0, "url")
    => "https://oaidalleapiprodscus.blob.core.windows.net/private/org-Rf437IxKhh..."

Moderations

Pass a string to check if it violates OpenAI's Content Policy:

    response = client.moderations(parameters: { input: "I'm worried about that." })
    puts response.dig("results", 0, "category_scores", "hate")
    => 5.505014632944949e-05

Development

After checking out the repo, run bin/setup to install dependencies. You can run bin/console for an interactive prompt that will allow you to experiment.

To install this gem onto your local machine, run bundle exec rake install.

Release

First run the specs without VCR so they actually hit the API. This will cost about 2 cents. You'll need to add your OPENAI_ACCESS_TOKEN= in .env.

  NO_VCR=true bundle exec rspec

Then update the version number in version.rb, update CHANGELOG.md, run bundle install to update Gemfile.lock, and then run bundle exec rake release, which will create a git tag for the version, push git commits and tags, and push the .gem file to rubygems.org.

Contributing

Bug reports and pull requests are welcome on GitHub at https://github.com/alexrudall/ruby-openai. This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the code of conduct.

License

The gem is available as open source under the terms of the MIT License.

Code of Conduct

Everyone interacting in the Ruby::OpenAI project's codebases, issue trackers, chat rooms and mailing lists is expected to follow the code of conduct.

kiosan / ruby-openai