Ruby OpenAI

gem "ruby-advanced-openai"

require "openai"

client = OpenAI::Client.new(access_token: "access_token_goes_here")

OpenAI.configure do |config|
    config.access_token = ENV.fetch("OPENAI_ACCESS_TOKEN")
    config.organization_id = ENV.fetch("OPENAI_ORGANIZATION_ID") # Optional.
end

client = OpenAI::Client.new

client = OpenAI::Client.new(
    access_token: "access_token_goes_here",
    uri_base: "https://oai.hconeai.com/",
    request_timeout: 240
)

OpenAI.configure do |config|
    config.access_token = ENV.fetch("OPENAI_ACCESS_TOKEN")
    config.organization_id = ENV.fetch("OPENAI_ORGANIZATION_ID") # Optional
    config.uri_base = "https://oai.hconeai.com/" # Optional
    config.request_timeout = 240 # Optional
end

client.models.list
client.models.retrieve(id: "text-ada-001")

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 may I assist you today?"

incoming_mail = <<~MESSAGE
拝啓、Hoge様

お世話になっております。私たちの会社は、〇〇に関する新規プロジェクトの立ち上げを検討しております。このたび、貴社に対して提案書の作成を依頼させていただきたく、ご連絡差し上げました。

ご承知の通り、弊社は〇〇分野での豊富な経験と実績を持つ企業です。今回のプロジェクトにおいても、貴社の専門知識と技術力に大変興味を持っており、提案書を通じて具体的な提案をいただければと考えています。

提案書の内容には、以下の要素を含めていただけると幸いです：

1. プロジェクトの目的と背景
2. 提案するソリューションやアプローチ
3. 予算と納期の見積もり
4. 弊社と貴社の連携方法やプロジェクトの進行管理について

なお、提案書の作成にあたり、詳細な要件や資料を別途提供させていただきます。また、提案書の提出期限は〇〇月〇〇日までとさせていただきますので、お時間の許す範囲でご検討いただければ幸いです。

お忙しい中恐縮ですが、貴社のご協力とご検討をお願い申し上げます。何卒、ご返信をいただけますようお願い申し上げます。

敬具
fuga株式会社
MESSAGE
response = client.suggested_reply(model: "gpt-4", incoming_mail: incoming_mail, reply_summary: "承知しました。")
puts response.dig("choices", 0, "message", "content")
# => {Email reply text}

client.chat(
    parameters: {
        model: "gpt-3.5-turbo", # Required.
        messages: [{ role: "user", content: "Describe a character called Anna!"}], # Required.
        temperature: 0.7,
        stream: proc do |chunk, _bytesize|
            print chunk.dig("choices", 0, "delta", "content")
        end
    })
# => "Anna is a young woman in her mid-twenties, with wavy chestnut hair that falls to her shoulders..."

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"]

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?

client.embeddings(
    parameters: {
        model: "babbage-similarity",
        input: "The food was delicious and the waiter..."
    }
)

client.files.upload(parameters: { file: "path/to/sentiment.jsonl", purpose: "fine-tune" })
client.files.list
client.files.retrieve(id: "file-123")
client.files.content(id: "file-123")
client.files.delete(id: "file-123")

response = client.files.upload(parameters: { file: "path/to/sentiment.jsonl", purpose: "fine-tune" })
file_id = JSON.parse(response.body)["id"]

response = client.finetunes.create(
    parameters: {
    training_file: file_id,
    model: "ada"
})
fine_tune_id = response["id"]

client.finetunes.cancel(id: fine_tune_id)

client.finetunes.list
response = client.finetunes.retrieve(id: fine_tune_id)
fine_tuned_model = response["fine_tuned_model"]

response = client.completions(
    parameters: {
        model: fine_tuned_model,
        prompt: "I love Mondays!"
    }
)
response.dig("choices", 0, "text")

client.finetunes.delete(fine_tuned_model: fine_tuned_model)

response = client.images.generate(parameters: { prompt: "A baby sea otter cooking pasta wearing a hat of some sort", size: "256x256" })
puts response.dig("data", 0, "url")
# => "https://oaidalleapiprodscus.blob.core.windows.net/private/org-Rf437IxKhh..."

response = client.images.edit(parameters: { prompt: "A solid red Ruby on a blue background", image: "image.png", mask: "mask.png" })
puts response.dig("data", 0, "url")
# => "https://oaidalleapiprodscus.blob.core.windows.net/private/org-Rf437IxKhh..."

response = client.images.variations(parameters: { image: "image.png", n: 2 })
puts response.dig("data", 0, "url")
# => "https://oaidalleapiprodscus.blob.core.windows.net/private/org-Rf437IxKhh..."

response = client.moderations(parameters: { input: "I'm worried about that." })
puts response.dig("results", 0, "category_scores", "hate")
# => 5.505014632944949e-05

response = client.translate(
    parameters: {
        model: "whisper-1",
        file: File.open("path_to_file", "rb"),
    })
puts response["text"]
# => "Translation of the text"

response = client.transcribe(
    parameters: {
        model: "whisper-1",
        file: File.open("path_to_file", "rb"),
    })
puts response["text"]
# => "Transcription of the text"