Use the OpenAI GPT-3 API with Ruby! 🤖❤️
Add this line to your application's Gemfile:
gem "ruby-openai"And then execute:
$ bundle install
Or install with:
$ gem install ruby-openai
and require with:
require "ruby/openai"Get your API key from https://beta.openai.com/account/api-keys
If you're using dotenv, you can add your secret key to your .env file:
OPENAI_ACCESS_TOKEN=access_token_goes_here
And create a client:
client = OpenAI::Client.newAlternatively you can pass your key directly to a new client:
client = OpenAI::Client.new(access_token: "access_token_goes_here")- GPT-3
- text-ada-001
- text-babbage-001
- text-curie-001
- text-davinci-001
- Codex (private beta)
- code-davinci-002
- code-cushman-001
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")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"]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?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..."
}
)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)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 classifications:
response = client.completions(
parameters: {
model: fine_tuned_model,
prompt: "I love Mondays!"
}
)
JSON.parse(response.body)["choices"].map { |c| c["text"] }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-05Pass examples and a query to predict the most likely labels:
response = client.classifications(parameters: {
examples: [
["A happy moment", "Positive"],
["I am sad.", "Negative"],
["I am feeling awesome", "Positive"]
],
query: "It is a raining day :(",
model: "text-ada-001"
})Or use the ID of a file you've uploaded:
response = client.classifications(parameters: {
file: "123abc,
query: "It is a raining day :(",
model: "text-ada-001"
})Pass documents, a question string, and an example question/response to get an answer to a question:
response = client.answers(parameters: {
documents: ["Puppy A is happy.", "Puppy B is sad."],
question: "which puppy is happy?",
model: "text-curie-001",
examples_context: "In 2017, U.S. life expectancy was 78.6 years.",
examples: [["What is human life expectancy in the United States?","78 years."]],
})Or use the ID of a file you've uploaded:
response = client.answers(parameters: {
file: "123abc",
question: "which puppy is happy?",
model: "text-curie-001",
examples_context: "In 2017, U.S. life expectancy was 78.6 years.",
examples: [["What is human life expectancy in the United States?","78 years."]],
})After checking out the repo, run bin/setup to install dependencies. Then, run rake spec to run the tests. You can also 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. To release a new version, update the version number in version.rb, update CHANGELOG.md, 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.
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.
The gem is available as open source under the terms of the MIT License.
Everyone interacting in the Ruby::OpenAI project's codebases, issue trackers, chat rooms and mailing lists is expected to follow the code of conduct.