OmniAI::OpenAI

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An OpenAI implementation of the OmniAI interface supporting ChatGPT, Whisper, Text-to-Voice, Voice-to-Text, and more. This library is community maintained.

Installation

gem install omniai-openai

Usage

Client

A client is setup as follows if ENV['OPENAI_API_KEY'] exists:

client = OmniAI::OpenAI::Client.new

A client may also be passed the following options:

  • api_key (required - default is ENV['OPENAI_API_KEY'])
  • api_prefix (optional) - used with a host when necessary
  • organization (optional)
  • project (optional)
  • host (optional) useful for usage with Ollama, LocalAI or other OpenAI API compatible services

Configuration

Global configuration is supported for the following options:

OmniAI::OpenAI.configure do |config|
  config.api_key = 'sk-...' # default: ENV['OPENAI_API_KEY']
  config.organization = '...' # default: ENV['OPENAI_ORGANIZATION']
  config.project = '...' # default: ENV['OPENAI_PROJECT']
  config.host = '...' # default: 'https://api.openai.com' - override for usage with LocalAI / Ollama
end

Usage with LocalAI

LocalAI offers built in compatability with the OpenAI specification. To initialize a client that points to a Ollama change the host accordingly:

client = OmniAI::OpenAI::Client.new(host: 'http://localhost:8080', api_key: nil)

For details on installation or running LocalAI see the getting started tutorial.

Usage with Ollama

Ollama offers built in compatability with the OpenAI specification. To initialize a client that points to a Ollama change the host accordingly:

client = OmniAI::OpenAI::Client.new(host: 'http://localhost:11434', api_key: nil)

For details on installation or running Ollama checkout the project README.

Usage with OpenRouter

Other fee-based systems/services have adopted all or some of the OpenAI API. For example open_router.ai is a web-services that provides access to many models and providers using their own as well as an OpenAI API.

client  = OmniAI::OpenAI::Client.new(
            host:       'https://open_router.ai',
            api_key:    ENV['OPENROUTER_API_KEY'],
            api_prefix: '/api')

Chat

A chat completion is generated by passing in a simple text prompt:

completion = client.chat('Tell me a joke!')
completion.content # 'Why did the chicken cross the road? To get to the other side.'

A chat completion may also be generated by using a prompt builder:

completion = client.chat do |prompt|
  prompt.system('Your are an expert in geography.')
  prompt.user('What is the capital of Canada?')
end
completion.content # 'The capital of Canada is Ottawa.'

Model

model takes an optional string (default is gtp-4o):

completion = client.chat('How fast is a cheetah?', model: OmniAI::OpenAI::Chat::Model::GPT_3_5_TURBO)
completion.content # 'A cheetah can reach speeds over 100 km/h.'

OpenAI API Reference model

Temperature

temperature takes an optional float between 0.0 and 2.0 (defaults is 0.7):

completion = client.chat('Pick a number between 1 and 5', temperature: 2.0)
completion.content # '3'

OpenAI API Reference temperature

Stream

stream takes an optional a proc to stream responses in real-time chunks instead of waiting for a complete response:

stream = proc do |chunk|
  print(chunk.content) # 'Better', 'three', 'hours', ...
end
client.chat('Be poetic.', stream:)

OpenAI API Reference stream

Format

format takes an optional symbol (:json) and that setes the response_format to json_object:

completion = client.chat(format: :json) do |prompt|
  prompt.system(OmniAI::Chat::JSON_PROMPT)
  prompt.user('What is the name of the drummer for the Beatles?')
end
JSON.parse(completion.content) # { "name": "Ringo" }

OpenAI API Reference response_format

When using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message.

Transcribe

A transcription is generated by passing in a path to a file:

transcription = client.transcribe(file.path)
transcription.text # '...'

Prompt

prompt is optional and can provide additional context for transcribing:

transcription = client.transcribe(file.path, prompt: '')
transcription.text # '...'

OpenAI API Reference prompt

Format

format is optional and supports json, text, srt or vtt:

transcription = client.transcribe(file.path, format: OmniAI::Transcribe::Format::TEXT)
transcription.text # '...'

OpenAI API Reference response_format

Language

language is optional and may improve accuracy and latency:

transcription = client.transcribe(file.path, language: OmniAI::Transcribe::Language::SPANISH)
transcription.text

OpenAI API Reference language

Temperature

temperature is optional and must be between 0.0 (more deterministic) and 1.0 (less deterministic):

transcription = client.transcribe(file.path, temperature: 0.2)
transcription.text

OpenAI API Reference temperature

Speak

Speech can be generated by passing text with a block:

File.open('example.ogg', 'wb') do |file|
  client.speak('How can a clam cram in a clean cream can?') do |chunk|
    file << chunk
  end
end

If a block is not provided then a tempfile is returned:

tempfile = client.speak('Can you can a can as a canner can can a can?')
tempfile.close
tempfile.unlink

Voice

voice is optional and must be one of the supported voices:

client.speak('She sells seashells by the seashore.', voice: OmniAI::OpenAI::Speak::Voice::SHIMMER)

OpenAI API Reference voice

Model

model is optional and must be either tts-1 or tts-1-hd (default):

client.speak('I saw a kitten eating chicken in the kitchen.', format: OmniAI::OpenAI::Speak::Model::TTS_1)

OpenAI API Refernce model

Speed

speed is optional and must be between 0.25 and 0.40:

client.speak('How much wood would a woodchuck chuck if a woodchuck could chuck wood?', speed: 4.0)

OmniAI API Reference speed

Format

format is optional and supports MP3 (default), OPUS, AAC, FLAC, WAV or PCM:

client.speak('A pessemistic pest exists amidst us.', format: OmniAI::OpenAI::Speak::Format::FLAC)

OpenAI API Reference format

Files

Finding an File

client.files.find(id: 'file_...')

Listing all Files

client.files.all

Uploading a File

Using a File

file = client.files.build(io: File.open('demo.pdf', 'wb'))
file.save!

Using a Path

file = client.files.build(io: 'demo.pdf'))
file.save!

Downloading a File

file = client.files.find(id: 'file_...')
File.open('...', 'wb') do |file|
  file.content do |chunk|
    file << chunk
  end
end

Destroying a File

client.files.destroy!('file_...')

Assistants

Finding an Assistant

client.assistants.find(id: 'asst_...')

Listing all Assistants

client.assistants.all

Creating an Assistant

assistant = client.assistants.build
assistant.name = 'Ringo'
assistant.model = OmniAI::OpenAI::Chat::Model::GPT_4
assistant.description = 'The drummer for the Beatles.'
assistant.save!

Updating an Assistant

assistant = client.assistants.find(id: 'asst_...')
assistant.name = 'George'
assistant.model = OmniAI::OpenAI::Chat::Model::GPT_4
assistant.description = 'A guitarist for the Beatles.'
assistant.save!

Destroying an Assistant

client.assistants.destroy!('asst_...')

Threads

Finding a Thread

client.threads.find(id: 'thread_...')

Creating a Thread

thread = client.threads.build
thread.metadata = { user: 'Ringo' }
thread.save!

Updating a Thread

thread = client.threads.find(id: 'thread_...')
thread.metadata = { user: 'Ringo' }
thread.save!

Destroying a Threads

client.threads.destroy!('thread_...')

Messages

Finding a Message

thread = client.threads.find(id: 'thread_...')
message = thread.messages.find(id: 'msg_...')
message.save!

Listing all Messages

thread = client.threads.find(id: 'thread_...')
thread.messages.all

Creating a Message

thread = client.threads.find(id: 'thread_...')
message = thread.messages.build(role: 'user', content: 'Hello?')
message.save!

Updating a Message

thread = client.threads.find(id: 'thread_...')
message = thread.messages.build(role: 'user', content: 'Hello?')
message.save!

Runs

Finding a Run

thread = client.threads.find(id: 'thread_...')
run = thread.runs.find(id: 'run_...')
run.save!

Listing all Runs

thread = client.threads.find(id: 'thread_...')
thread.runs.all

Creating a Run

run = client.runs.find(id: 'thread_...')
run = thread.runs.build
run.metadata = { user: 'Ringo' }
run.save!

Updating a Run

thread = client.threads.find(id: 'thread_...')
run = thread.messages.find(id: 'run_...')
run.metadata = { user: 'Ringo' }
run.save!

Polling a Run

run.terminated? # false
run.poll!
run.terminated? # true
run.status # 'cancelled' / 'failed' / 'completed' / 'expired'

Cancelling a Run

thread = client.threads.find(id: 'thread_...')
run = thread.runs.cancel!(id: 'run_...')

Embed

Text can be converted into a vector embedding for similarity comparison usage via:

response = client.embed('The quick brown fox jumps over a lazy dog.')
response.embedding # [0.0, ...]