gem "ruby-openai"

require "openai"

client = OpenAI::Client.new(
  access_token: "access_token_goes_here",
  log_errors: true # Highly recommended in development, so you can see what errors OpenAI is returning. Not recommended in production because it could leak private data to your logs.
)

OpenAI.configure do |config|
  config.access_token = ENV.fetch("OPENAI_ACCESS_TOKEN")
  config.organization_id = ENV.fetch("OPENAI_ORGANIZATION_ID") # Optional
  config.log_errors = true # Highly recommended in development, so you can see what errors OpenAI is returning. Not recommended in production because it could leak private data to your logs.
end

client = OpenAI::Client.new

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

client = OpenAI::Client.new(
  access_token: "access_token_goes_here",
  uri_base: "https://oai.hconeai.com/",
  request_timeout: 240,
  extra_headers: {
    "X-Proxy-TTL" => "43200", # For https://github.com/6/openai-caching-proxy-worker#specifying-a-cache-ttl
    "X-Proxy-Refresh": "true", # For https://github.com/6/openai-caching-proxy-worker#refreshing-the-cache
    "Helicone-Auth": "Bearer HELICONE_API_KEY", # For https://docs.helicone.ai/getting-started/integration-method/openai-proxy
    "helicone-stream-force-format" => "true", # Use this with Helicone otherwise streaming drops chunks # https://github.com/alexrudall/ruby-openai/issues/251
  }
)

OpenAI.configure do |config|
  config.access_token = ENV.fetch("OPENAI_ACCESS_TOKEN")
  config.log_errors = true # Optional
  config.organization_id = ENV.fetch("OPENAI_ORGANIZATION_ID") # Optional
  config.uri_base = "https://oai.hconeai.com/" # Optional
  config.request_timeout = 240 # Optional
  config.extra_headers = {
    "X-Proxy-TTL" => "43200", # For https://github.com/6/openai-caching-proxy-worker#specifying-a-cache-ttl
    "X-Proxy-Refresh": "true", # For https://github.com/6/openai-caching-proxy-worker#refreshing-the-cache
    "Helicone-Auth": "Bearer HELICONE_API_KEY" # For https://docs.helicone.ai/getting-started/integration-method/openai-proxy
  } # Optional
end

client = OpenAI::Client.new(access_token: "access_token_goes_here")
client.add_headers("X-Proxy-TTL" => "43200")

client = OpenAI::Client.new(log_errors: true)

client = OpenAI::Client.new do |f|
  f.response :logger, Logger.new($stdout), bodies: true
end

OpenAI.configure do |config|
  config.access_token = ENV.fetch("AZURE_OPENAI_API_KEY")
  config.uri_base = ENV.fetch("AZURE_OPENAI_URI")
  config.api_type = :azure
  config.api_version = "2023-03-15-preview"
end

client = OpenAI::Client.new(
  uri_base: "http://localhost:11434"
)

client.chat(
  parameters: {
    model: "llama3", # Required.
    messages: [{ role: "user", content: "Hello!"}], # Required.
    temperature: 0.7,
    stream: proc do |chunk, _bytesize|
      print chunk.dig("choices", 0, "delta", "content")
    end
  }
)

# => Hi! It's nice to meet you. Is there something I can help you with, or would you like to chat?

client = OpenAI::Client.new(
  access_token: "groq_access_token_goes_here",
  uri_base: "https://api.groq.com/openai"
)

client.chat(
  parameters: {
    model: "llama3-8b-8192", # Required.
    messages: [{ role: "user", content: "Hello!"}], # Required.
    temperature: 0.7,
    stream: proc do |chunk, _bytesize|
     print chunk.dig("choices", 0, "delta", "content")
    end
  }
)

OpenAI.rough_token_count("Your text")

client.models.list
client.models.retrieve(id: "gpt-4o")

response = client.chat(
  parameters: {
    model: "gpt-4o", # Required.
    messages: [{ role: "user", content: "Hello!"}], # Required.
    temperature: 0.7,
  }
)
puts response.dig("choices", 0, "message", "content")
# => "Hello! How may I assist you today?"

client.chat(
  parameters: {
    model: "gpt-4o", # 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..."

stream_proc = proc { |chunk, _bytesize| puts "--------------"; puts chunk.inspect; }
client.chat(
  parameters: {
    model: "gpt-4o",
    stream: stream_proc,
    stream_options: { include_usage: true },
    messages: [{ role: "user", content: "Hello!"}],
  }
)
# => --------------
# => {"id"=>"chatcmpl-7bbq05PiZqlHxjV1j7OHnKKDURKaf", "object"=>"chat.completion.chunk", "created"=>1718750612, "model"=>"gpt-4o-2024-05-13", "system_fingerprint"=>"fp_9cb5d38cf7", "choices"=>[{"index"=>0, "delta"=>{"role"=>"assistant", "content"=>""}, "logprobs"=>nil, "finish_reason"=>nil}], "usage"=>nil}
# => --------------
# => {"id"=>"chatcmpl-7bbq05PiZqlHxjV1j7OHnKKDURKaf", "object"=>"chat.completion.chunk", "created"=>1718750612, "model"=>"gpt-4o-2024-05-13", "system_fingerprint"=>"fp_9cb5d38cf7", "choices"=>[{"index"=>0, "delta"=>{"content"=>"Hello"}, "logprobs"=>nil, "finish_reason"=>nil}], "usage"=>nil}
# => --------------
# => ... more content chunks
# => --------------
# => {"id"=>"chatcmpl-7bbq05PiZqlHxjV1j7OHnKKDURKaf", "object"=>"chat.completion.chunk", "created"=>1718750612, "model"=>"gpt-4o-2024-05-13", "system_fingerprint"=>"fp_9cb5d38cf7", "choices"=>[{"index"=>0, "delta"=>{}, "logprobs"=>nil, "finish_reason"=>"stop"}], "usage"=>nil}
# => --------------
# => {"id"=>"chatcmpl-7bbq05PiZqlHxjV1j7OHnKKDURKaf", "object"=>"chat.completion.chunk", "created"=>1718750612, "model"=>"gpt-4o-2024-05-13", "system_fingerprint"=>"fp_9cb5d38cf7", "choices"=>[], "usage"=>{"prompt_tokens"=>9, "completion_tokens"=>9, "total_tokens"=>18}}

messages = [
  { "type": "text", "text": "What’s in this image?"},
  { "type": "image_url",
    "image_url": {
      "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg",
    },
  }
]
response = client.chat(
  parameters: {
    model: "gpt-4-vision-preview", # Required.
    messages: [{ role: "user", content: messages}], # Required.
  }
)
puts response.dig("choices", 0, "message", "content")
# => "The image depicts a serene natural landscape featuring a long wooden boardwalk extending straight ahead"

response = client.chat(
  parameters: {
    model: "gpt-4o",
    response_format: { type: "json_object" },
    messages: [{ role: "user", content: "Hello! Give me some JSON please."}],
    temperature: 0.7,
  })
  puts response.dig("choices", 0, "message", "content")
  # =>
  # {
  #   "name": "John",
  #   "age": 30,
  #   "city": "New York",
  #   "hobbies": ["reading", "traveling", "hiking"],
  #   "isStudent": false
  # }

  response = client.chat(
    parameters: {
      model: "gpt-4o",
      messages: [{ role: "user", content: "Can I have some JSON please?"}],
      response_format: { type: "json_object" },
      stream: proc do |chunk, _bytesize|
        print chunk.dig("choices", 0, "delta", "content")
      end
    }
  )
  # =>
  # {
  #   "message": "Sure, please let me know what specific JSON data you are looking for.",
  #   "JSON_data": {
  #     "example_1": {
  #       "key_1": "value_1",
  #       "key_2": "value_2",
  #       "key_3": "value_3"
  #     },
  #     "example_2": {
  #       "key_4": "value_4",
  #       "key_5": "value_5",
  #       "key_6": "value_6"
  #     }
  #   }
  # }

def get_current_weather(location:, unit: "fahrenheit")
  # Here you could use a weather api to fetch the weather.
  "The weather in #{location} is nice 🌞 #{unit}"
end

messages = [
  {
    "role": "user",
    "content": "What is the weather like in San Francisco?",
  },
]

response =
  client.chat(
    parameters: {
      model: "gpt-4o",
      messages: messages,  # Defined above because we'll use it again
      tools: [
        {
          type: "function",
          function: {
            name: "get_current_weather",
            description: "Get the current weather in a given location",
            parameters: {  # Format: https://json-schema.org/understanding-json-schema
              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"],
            },
          },
        }
      ],
      # Optional, defaults to "auto"
      # Can also put "none" or specific functions, see docs
      tool_choice: "required"
    },
  )

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

if message["role"] == "assistant" && message["tool_calls"]
  message["tool_calls"].each do |tool_call|
    tool_call_id = tool_call.dig("id")
    function_name = tool_call.dig("function", "name")
    function_args = JSON.parse(
      tool_call.dig("function", "arguments"),
      { symbolize_names: true },
    )
    function_response =
      case function_name
      when "get_current_weather"
        get_current_weather(**function_args)  # => "The weather is nice 🌞"
      else
        # decide how to handle
      end

    # For a subsequent message with the role "tool", OpenAI requires the preceding message to have a tool_calls argument.
    messages << message

    messages << {
      tool_call_id: tool_call_id,
      role: "tool",
      name: function_name,
      content: function_response
    }  # Extend the conversation with the results of the functions
  end

  second_response = client.chat(
    parameters: {
      model: "gpt-4o",
      messages: messages
    }
  )

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

  # At this point, the model has decided to call functions, you've called the functions
  # and provided the response back, and the model has considered this and responded.
end
# => "It looks like the weather is nice and sunny in San Francisco! If you're planning to go out, it should be a pleasant day."

response = client.completions(
  parameters: {
    model: "gpt-4o",
    prompt: "Once upon a time",
    max_tokens: 5
  }
)
puts response["choices"].map { |c| c["text"] }
# => [", there lived a great"]

response = client.embeddings(
  parameters: {
    model: "text-embedding-ada-002",
    input: "The food was delicious and the waiter..."
  }
)

puts response.dig("data", 0, "embedding")
# => Vector representation of your embedding

response = client.batches.create(
  parameters: {
    input_file_id: "file-abc123",
    endpoint: "/v1/chat/completions",
    completion_window: "24h"
  }
)
batch_id = response["id"]

batch = client.batches.retrieve(id: batch_id)

client.batches.cancel(id: batch_id)

client.batches.list

batch = client.batches.retrieve(id: batch_id)
output_file_id = batch["output_file_id"]
output_response = client.files.content(id: output_file_id)
error_file_id = batch["error_file_id"]
error_response = client.files.content(id: error_file_id)

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")

client.files.upload(parameters: { file: "path/to/file.pdf", purpose: "assistants" })

my_file = File.open("path/to/file.pdf", "rb")
client.files.upload(parameters: { file: my_file, purpose: "assistants" })

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

response = client.finetunes.create(
  parameters: {
  training_file: file_id,
  model: "gpt-4o"
})
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.chat(
  parameters: {
    model: fine_tuned_model,
    messages: [{ role: "user", content: "I love Mondays!" }]
  }
)
response.dig("choices", 0, "message", "content")

client.finetunes.list_events(id: fine_tune_id)

response = client.vector_stores.create(
  parameters: {
    name: "my vector store",
    file_ids: ["file-abc123", "file-def456"]
  }
)

vector_store_id = response["id"]

client.vector_stores.retrieve(id: vector_store_id)

client.vector_stores.list

response = client.vector_stores.modify(
  id: vector_store_id,
  parameters: {
    name: "Modified Test Vector Store",
  }
)

client.vector_stores.delete(id: vector_store_id)

response = client.vector_store_files.create(
  vector_store_id: "vector-store-abc123",
  parameters: {
    file_id: "file-abc123"
  }
)

vector_store_file_id = response["id"]

client.vector_store_files.retrieve(
  vector_store_id: "vector-store-abc123",
  id: vector_store_file_id
)

client.vector_store_files.list(vector_store_id: "vector-store-abc123")

client.vector_store_files.delete(
  vector_store_id: "vector-store-abc123",
  id: vector_store_file_id
)

response = client.vector_store_file_batches.create(
  vector_store_id: "vector-store-abc123",
  parameters: {
    file_ids: ["file-abc123", "file-def456"]
  }
)

file_batch_id = response["id"]

client.vector_store_file_batches.retrieve(
  vector_store_id: "vector-store-abc123",
  id: file_batch_id
)

client.vector_store_file_batches.list(
  vector_store_id: "vector-store-abc123",
  id: file_batch_id
)

client.vector_store_file_batches.cancel(
  vector_store_id: "vector-store-abc123",
  id: file_batch_id
)

response = client.assistants.create(
  parameters: {
    model: "gpt-4o",
    name: "OpenAI-Ruby test assistant",
    description: nil,
    instructions: "You are a Ruby dev bot. When asked a question, write and run Ruby code to answer the question",
    tools: [
      { type: "code_interpreter" },
      { type: "file_search" }
    ],
    tool_resources: {
      code_interpreter: {
        file_ids: [] # See Files section above for how to upload files
      },
      file_search: {
        vector_store_ids: [] # See Vector Stores section above for how to add vector stores
      }
    },
    "metadata": { my_internal_version_id: "1.0.0" }
  }
)
assistant_id = response["id"]

client.assistants.retrieve(id: assistant_id)

client.assistants.list

response = client.assistants.modify(
  id: assistant_id,
  parameters: {
    name: "Modified Test Assistant for OpenAI-Ruby",
    metadata: { my_internal_version_id: '1.0.1' }
  }
)

client.assistants.delete(id: assistant_id)

# Create thread
response = client.threads.create # Note: Once you create a thread, there is no way to list it
                                 # or recover it currently (as of 2023-12-10). So hold onto the `id`
thread_id = response["id"]

# Add initial message from user (see https://platform.openai.com/docs/api-reference/messages/createMessage)
message_id = client.messages.create(
  thread_id: thread_id,
  parameters: {
    role: "user", # Required for manually created messages
    content: "Can you help me write an API library to interact with the OpenAI API please?"
  }
)["id"]

# Retrieve individual message
message = client.messages.retrieve(thread_id: thread_id, id: message_id)

# Review all messages on the thread
messages = client.messages.list(thread_id: thread_id)

# To delete the thread (and all associated messages):
client.threads.delete(id: thread_id)

client.messages.retrieve(thread_id: thread_id, id: message_id) # -> Fails after thread is deleted

# Create run (will use instruction/model/tools from Assistant's definition)
response = client.runs.create(
  thread_id: thread_id,
  parameters: {
    assistant_id: assistant_id,
    max_prompt_tokens: 256,
    max_completion_tokens: 16
  }
)
run_id = response['id']

client.runs.create(
  thread_id: thread_id,
  parameters: {
    assistant_id: assistant_id,
    max_prompt_tokens: 256,
    max_completion_tokens: 16,
    stream: proc do |chunk, _bytesize|
      if chunk["object"] == "thread.message.delta"
        print chunk.dig("delta", "content", 0, "text", "value")
      end
    end
  }
)

response = client.runs.retrieve(id: run_id, thread_id: thread_id)
status = response['status']

while true do
  response = client.runs.retrieve(id: run_id, thread_id: thread_id)
  status = response['status']

  case status
  when 'queued', 'in_progress', 'cancelling'
    puts 'Sleeping'
    sleep 1 # Wait one second and poll again
  when 'completed'
    break # Exit loop and report result to user
  when 'requires_action'
    # Handle tool calls (see below)
  when 'cancelled', 'failed', 'expired'
    puts response['last_error'].inspect
    break # or `exit`
  else
    puts "Unknown status response: #{status}"
  end
end

# Either retrieve all messages in bulk again, or...
messages = client.messages.list(thread_id: thread_id, parameters: { order: 'asc' })

# Alternatively retrieve the `run steps` for the run which link to the messages:
run_steps = client.run_steps.list(thread_id: thread_id, run_id: run_id, parameters: { order: 'asc' })
new_message_ids = run_steps['data'].filter_map do |step|
  if step['type'] == 'message_creation'
    step.dig('step_details', "message_creation", "message_id")
  end # Ignore tool calls, because they don't create new messages.
end

# Retrieve the individual messages
new_messages = new_message_ids.map do |msg_id|
  client.messages.retrieve(id: msg_id, thread_id: thread_id)
end

# Find the actual response text in the content array of the messages
new_messages.each do |msg|
  msg['content'].each do |content_item|
    case content_item['type']
    when 'text'
      puts content_item.dig('text', 'value')
      # Also handle annotations
    when 'image_file'
      # Use File endpoint to retrieve file contents via id
      id = content_item.dig('image_file', 'file_id')
    end
  end
end

metadata = {
  user_id: "abc123"
}
message = client.messages.modify(
  id: message_id,
  thread_id: thread_id,
  parameters: { metadata: metadata },
)

client.runs.list(thread_id: thread_id, parameters: { order: "asc", limit: 3 })

response = client.runs.create_thread_and_run(parameters: { assistant_id: assistant_id })
run_id = response['id']
thread_id = response['thread_id']

require "openai"

# Make a client
client = OpenAI::Client.new(
  access_token: "access_token_goes_here",
  log_errors: true # Don't log errors in production.
)

# Upload image as a file
file_id = client.files.upload(
  parameters: {
    file: "path/to/example.png",
    purpose: "assistants",
  }
)["id"]

# Create assistant
assistant_id = client.assistants.create(
  parameters: {
    model: "gpt-4o",
    name: "Image reader",
    instructions: "You are an image describer. You describe the contents of images.",
  }
)["id"]

# Create thread
thread_id = client.threads.create["id"]

# Add image in message
client.messages.create(
  thread_id: thread_id,
  parameters: {
    role: "user", # Required for manually created messages
    content: [
      {
        "type": "text",
        "text": "What's in this image?"
      },
      {
        "type": "image_file",
        "image_file": { "file_id": file_id }
      }
    ]
  }
)

# Run thread
run_id = client.runs.create(
  thread_id: thread_id,
  parameters: { assistant_id: assistant_id }
)["id"]

# Wait until run in complete
status = nil
until status == "completed" do
  sleep(0.1)
  status = client.runs.retrieve(id: run_id, thread_id: thread_id)['status']
end

# Get the response
messages = client.messages.list(thread_id: thread_id, parameters: { order: 'asc' })
messages.dig("data", -1, "content", 0, "text", "value")
=> "The image contains a placeholder graphic with a tilted, stylized representation of a postage stamp in the top part, which includes an abstract landscape with hills and a sun. Below the stamp, in the middle of the image, there is italicized text in a light golden color that reads, \"This is just an example.\" The background is a light pastel shade, and a yellow border frames the entire image."

def get_current_weather(location:, unit: "celsius")
  # Your function code goes here
  if location =~ /San Francisco/i
    return unit == "celsius" ? "The weather is nice 🌞 at 27°C" : "The weather is nice 🌞 at 80°F"
  else
    return unit == "celsius" ? "The weather is icy 🥶 at -5°C" : "The weather is icy 🥶 at 23°F"
  end
end

if status == 'requires_action'
  tools_to_call = response.dig('required_action', 'submit_tool_outputs', 'tool_calls')

  my_tool_outputs = tools_to_call.map { |tool|
    # Call the functions based on the tool's name
    function_name = tool.dig('function', 'name')
    arguments = JSON.parse(
      tool.dig("function", "arguments"),
      { symbolize_names: true },
    )

    tool_output = case function_name
    when "get_current_weather"
      get_current_weather(**arguments)
    end

    {
      tool_call_id: tool['id'],
      output: tool_output,
    }
  }

  client.runs.submit_tool_outputs(
    thread_id: thread_id,
    run_id: run_id,
    parameters: { tool_outputs: my_tool_outputs }
  )
end

require "openai"

# Make a client
client = OpenAI::Client.new(
  access_token: "access_token_goes_here",
  log_errors: true # Don't log errors in production.
)

# Upload your file(s)
file_id = client.files.upload(
  parameters: {
    file: "path/to/somatosensory.pdf",
    purpose: "assistants"
  }
)["id"]

# Create a vector store to store the vectorised file(s)
vector_store_id = client.vector_stores.create(parameters: {})["id"]

# Vectorise the file(s)
vector_store_file_id = client.vector_store_files.create(
  vector_store_id: vector_store_id,
  parameters: { file_id: file_id }
)["id"]

# Check that the file is vectorised (wait for status to be "completed")
client.vector_store_files.retrieve(vector_store_id: vector_store_id, id: vector_store_file_id)["status"]

# Create an assistant, referencing the vector store
assistant_id = client.assistants.create(
  parameters: {
    model: "gpt-4o",
    name: "Answer finder",
    instructions: "You are a file search tool. Find the answer in the given files, please.",
    tools: [
      { type: "file_search" }
    ],
    tool_resources: {
      file_search: {
        vector_store_ids: [vector_store_id]
      }
    }
  }
)["id"]

# Create a thread with your question
thread_id = client.threads.create(parameters: {
  messages: [
    { role: "user",
      content: "Find the description of a nociceptor." }
  ]
})["id"]

# Run the thread to generate the response. Include the "GIVE ME THE CHUNKS" incantation.
run_id = client.runs.create(
  thread_id: thread_id,
  parameters: {
    assistant_id: assistant_id
  },
  query_parameters: { include: ["step_details.tool_calls[*].file_search.results[*].content"] } # incantation
)["id"]

# Get the steps that happened in the run
steps = client.run_steps.list(
  thread_id: thread_id,
  run_id: run_id,
  parameters: { order: "asc" }
)

# Retrieve all the steps. Include the "GIVE ME THE CHUNKS" incantation again.
steps = steps["data"].map do |step|
  client.run_steps.retrieve(
    thread_id: thread_id,
    run_id: run_id,
    id: step["id"],
    parameters: { include: ["step_details.tool_calls[*].file_search.results[*].content"] } # incantation
  )
end

# Now we've got the chunk info, buried deep. Loop through the steps and find chunks if included:
chunks = steps.flat_map do |step|
  included_results = step.dig("step_details", "tool_calls", 0, "file_search", "results")

  next if included_results.nil? || included_results.empty?

  included_results.flat_map do |result|
    result["content"].map do |content|
      content["text"]
    end
  end
end.compact

# The first chunk will be the closest match to the prompt. Finally, if you want to view the completed message(s):
client.messages.list(thread_id: thread_id)

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.generate(
  parameters: {
    prompt: "A springer spaniel cooking pasta wearing a hat of some sort",
    model: "dall-e-3",
    size: "1024x1792",
    quality: "standard",
  }
)
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.audio.translate(
  parameters: {
    model: "whisper-1",
    file: File.open("path_to_file", "rb"),
  }
)
puts response["text"]
# => "Translation of the text"

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

response = client.audio.speech(
  parameters: {
    model: "tts-1",
    input: "This is a speech test!",
    voice: "alloy",
    response_format: "mp3", # Optional
    speed: 1.0, # Optional
  }
)
File.binwrite('demo.mp3', response)
# => mp3 file that plays: "This is a speech test!"

begin
  OpenAI::Client.new.models.retrieve(id: "gpt-4o")
rescue Faraday::Error => e
  raise "Got a Faraday error: #{e}"
end