Class: Lammy::OpenAI

Inherits:
Object
  • Object
show all
Defined in:
lib/lammy/openai.rb

Overview

Use the OpenAI API’s Ruby library

Constant Summary collapse

MODELS = 
[
  /^gpt-.*$/,
  /^chatgpt-.*$/,
  /^o\d+(?:-.*)?$/,
  /^(?:davinci|babbage)-002$/
].freeze
EMBEDDINGS = 
%w[
  text-embedding-3-small text-embedding-3-large text-embedding-ada-002
].freeze

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(settings) ⇒ OpenAI

Returns a new instance of OpenAI.



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# File 'lib/lammy/openai.rb', line 23

def initialize(settings)
  @settings = settings
end

Instance Attribute Details

#settings ⇒ Object (readonly)

Returns the value of attribute settings.



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# File 'lib/lammy/openai.rb', line 21

def settings
  @settings
end

Instance Method Details

#chat(user_message, system_message = nil, stream = nil) ⇒ Object

Generate a response with support for structured output



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# File 'lib/lammy/openai.rb', line 28

def chat(user_message, system_message = nil, stream = nil)
  schema = schema(settings)
  messages = messages(user_message, system_message)

  request = client.chat(
    parameters: {
      model: settings[:model] || Lammy.configuration.model,
      response_format: schema,
      messages: messages,
      stream: stream ? ->(chunk) { stream.call(stream_content(chunk)) } : nil
    }.compact
  )

  return stream if stream

  response = request.dig('choices', 0, 'message', 'content')
  content = schema ? ::Hashie::Mash.new(JSON.parse(response)) : response
  array?(schema) ? content.items : content
end

#embeddings(chunks) ⇒ Object

OpenAI’s text embeddings measure the relatedness of text strings. An embedding is a vector of floating point numbers. The distance between two vectors measures their relatedness. Small distances suggest high relatedness and large distances suggest low relatedness.



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# File 'lib/lammy/openai.rb', line 51

def embeddings(chunks)
  responses = chunks.map do |chunk|
    response = client.embeddings(
      parameters: { model: settings[:model], dimensions: settings[:dimensions], input: chunk }
    )

    response.dig('data', 0, 'embedding')
  end

  responses.one? ? responses.first : responses
end