openai functions and embeddings

Basic

require "openai"

OpenAI.configure do |config|
  config.access_token = ENV.fetch('OPENAI_ACCESS_TOKEN', nil)
end

client = OpenAI::Client.new

response = client.chat(
    parameters: {
        model: "gpt-3.5-turbo", # Required.
        messages: [{ role: "user", content: "Hello!"}], # Required.
        temperature: 0.7,
    })
puts response.dig("choices", 0, "message", "content")

Hello! How can I assist you today?

Functions

def get_current_weather(location:, unit: "fahrenheit")
  puts "#{location}, #{unit}"
  # use a weather api to fetch weather
end

response =
  client.chat(
    parameters: {
      model: "gpt-3.5-turbo-0613",
      messages: [
        {
          "role": "user",
          "content": "What is the weather like in San Francisco?",
        },
      ],
      functions: [
        {
          name: "get_current_weather",
          description: "Get the current weather in a given location",
          parameters: {
            type: :object,
            properties: {
              location: {
                type: :string,
                description: "The city and state, e.g. San Francisco, CA",
              },
              unit: {
                type: "string",
                enum: %w[celsius fahrenheit],
              },
            },
            required: ["location"],
          },
        },
      ],
    },
  )

message = response.dig("choices", 0, "message")

puts message

if message["role"] == "assistant" && message["function_call"]
  function_name = message.dig("function_call", "name")
  args =
    JSON.parse(
      message.dig("function_call", "arguments"),
      { symbolize_names: true },
    )

  case function_name
  when "get_current_weather"
    get_current_weather(**args)
  end
end

{"role"=>"assistant", "content"=>nil, "function_call"=>{"name"=>"get_current_weather", "arguments"=>"{\n  \"location\": \"San Francisco, CA\"\n}"}}
San Francisco, CA, fahrenheit

Embeddings

response = client.embeddings(
    parameters: {
        model: "text-embedding-ada-002",
        input: "The king is naked, the king's ears are donkey ears..."
    }
)

puts response.dig("data", 0, "embedding")

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text_array = []

Dir.glob("training_data/*.txt") do |file|
  text = File.read(file).dump()
  text_array << text
end

embedding_array = []

text_array.each do |text|
  response = client.embeddings(
    parameters: {
      model: "text-embedding-ada-002",
      input: text
    }
  )

  embedding = response['data'][0]['embedding']
  embedding_hash = {embedding: embedding, text: text}
  embedding_array << embedding_hash
end

["\"OpenAI is an AGI research and deployment company. Our mission is to ensure that artificial general intelligence benefits all of humanity.\"", "\"OpenAI\\u2019s large language models, including the models that power ChatGPT, are developed using three primary sources of information: (1) information that is publicly available on the internet, (2) information that we license from third parties, and (3) information that our users or our human trainers provide.\""]

require 'csv'

CSV.open("embeddings.csv", "w") do |csv|
  csv << [:embedding, :text]
  embedding_array.each do |obj|
    csv << [obj[:embedding], obj[:text]]
  end
end

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0.007884575, 0.0053057857, 0.0068240766, -0.016467366, -0.020788403, -0.014873326, 0.0033181745, -0.023528574, -0.008036075, -0.025596876], :text=>"\"OpenAI\\u2019s large language models, including the models that power ChatGPT, are developed using three primary sources of information: (1) information that is publicly available on the internet, (2) information that we license from third parties, and (3) information that our users or our human trainers provide.\""}]

require 'cosine_similarity'

question = "What is open AI"

response = client.embeddings(
  parameters: {
    model: "text-embedding-ada-002",
    input: question
  }
)

question_embedding = response['data'][0]['embedding']

similarity_array = []

CSV.foreach("embeddings.csv", headers: true) do |row|
  text_embedding = JSON.parse(row['embedding'])
  similarity_array << cosine_similarity(question_embedding, text_embedding)
end

index_of_max = similarity_array.index(similarity_array.max)
original_text = ""

CSV.foreach("embeddings.csv", headers: true).with_index do |row, rowno|
  if rowno == index_of_max
    original_text = row['text']
  end
end

prompt =
"You are an AI assistant. You work for Sterling Parts which is a car parts online store located in Australia.
You will be asked questions from a customer and will answer in a helpful and friendly manner.

You will be provided company information from Sterline Parts under the [Article] section. The customer question
will be provided unders the [Question] section. You will answer the customers questions based on the article.
If the users question is not answered by the article you will respond with 'I'm sorry I don't know.'

[Article]
#{original_text}

[Question]
#{question}"

response = client.completions(
  parameters: {
    model: "text-davinci-003",
    prompt: prompt,
    temperature: 0.2,
    max_tokens: 500,
  }
)

puts response['choices']

response = client.chat(
  parameters: {
    model: "gpt-3.5-turbo",
    messages: [{role: "user", content: prompt}],
    temperature: 0.7,
  }
)

puts response.dig("choices", 0, "message", "content")

[Answer]
OpenAI is an AGI research and deployment company whose mission is to ensure that artificial general intelligence benefits all of humanity.