Ruby Pinecone

A Ruby gem for semantic search using Pinecone and OpenAI API. This gem provides out-of-the-box functionality for generating vectors using the OpenAI API's text-embedding-ada-002 model and uploading them to Pinecone for efficient semantic search. It also includes features for querying existing vectors and retrieving relevant metadata. With this gem, developers can easily incorporate semantic search capabilities into their Ruby applications without needing to worry about the underlying implementation details.

Install the gem and add to the application's Gemfile by executing:

$ bundle add ruby-openai-pinecone

If bundler is not being used to manage dependencies, install the gem by executing:

$ gem install ruby-openai-pinecone

You can find instructions here - ruby-openai-pinecone/examples/test.rb

1. Sign up for an OpenAI account at https://beta.openai.com/signup/.
2. After signing up, go to the API Keys section in your OpenAI account: https://platform.openai.com/account/api-keys.
3. Click on "Create an API key" and follow the instructions to generate your API key.
4. Use the generated API key for the config.openai_api_key in your Ruby application.

1. Sign up for a Pinecone account at https://www.pinecone.io/start/.
2. After signing up, go to the Dashboard.
3. Click on "Create Namespace" and follow the instructions to create a namespace for your application.
4. After creating a namespace, you will see your Pinecone API key, upsert endpoint, and query endpoint.
5. Use the API key for config.pinecone_api_key, upsert endpoint for config.pinecone_upsert_endpoint, and query endpoint for config.pinecone_query_endpoint in your Ruby application.

Let's say we want to create a simple book recommendation engine using the Ruby OpenAI Pinecone gem. We'll start by creating vectors for a few books, then query the engine to get recommendations based on a user's interests.

Before you begin, make sure to set up your Pinecone and OpenAI API configurations. Example in examples/test.rb

1. First, create some book data:

books = [
  {
    id: "book_1",
    title: "The Catcher in the Rye",
    description: "The story of teenage angst and self-discovery, narrated by the protagonist Holden Caulfield. The book delves into themes of identity, belonging, and the painful process of growing up.",
  },
  {
    id: "book_2",
    title: "To Kill a Mockingbird",
    description: "A novel about racial inequality and moral growth, set in the American South during the 1930s. Through the eyes of young Scout Finch, the reader witnesses the struggle for justice in a divided community.",
  },
  {
    id: "book_3",
    title: "1984",
    description: "A dystopian novel exploring the dangers of totalitarianism, surveillance, and loss of individuality. Set in the grim world of Airstrip One, the story follows Winston Smith as he tries to break free from the control of Big Brother.",
  },
  {
    id: "book_4",
    title: "Pride and Prejudice",
    description: "A romantic novel that follows the emotional development of protagonist Elizabeth Bennet and her suitor, Mr. Darcy. Set in early 19th-century England, the story explores themes of love, marriage, and social expectations.",
  },
  {
    id: "book_5",
    title: "Moby Dick",
    description: "A tale of obsession and revenge, as Captain Ahab hunts the elusive white whale, Moby Dick. The novel explores themes of fate, free will, and the struggle between good and evil.",
  }
]

2. Generate vectors for each book title and description, then upsert them to Pinecone:

books.each do |book|
  text = "#{book[:title]}: #{book[:description]}"
  vector = Ruby::OpenaiPinecone::Embeddings.generate(text)
  Ruby::OpenaiPinecone::Client.upsert(book[:id], vector)
end

3. Query the recommendation engine based on a user's interests:

user_interest = "I'm looking for a book about personal growth and the fight against injustice."

results = Ruby::OpenaiPinecone::Client.query(user_interest)

4. Show the top recommended books:

puts "Recommended books:"
results["matches"].each_with_index do |match, index|
  book = books.find { |b| b[:id] == match["id"] }
  puts "#{index + 1}. #{book[:title]} (Score: #{match["score"]})"
end

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.

Bug reports and pull requests are welcome on GitHub at https://github.com/mcheemaa/ruby-openai-pinecone. 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::Pinecone project's codebases, issue trackers, chat rooms and mailing lists is expected to follow the code of conduct.