Class: LangGraphRB::ChatRubyOpenAI

Inherits:
LLMBase
  • Object
show all
Defined in:
lib/langgraph_rb/chat_ruby_openai.rb

Instance Attribute Summary

Attributes inherited from LLMBase

#model, #temperature

Instance Method Summary collapse

Methods inherited from LLMBase

#bound_tools, #set_observers

Constructor Details

#initialize(model:, temperature: 0.0, api_key: ENV['OPENAI_API_KEY'], client: nil) ⇒ ChatRubyOpenAI

Returns a new instance of ChatRubyOpenAI.



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# File 'lib/langgraph_rb/chat_ruby_openai.rb', line 11

def initialize(model:, temperature: 0.0, api_key: ENV['OPENAI_API_KEY'], client: nil)
  super(model: model, temperature: temperature)
  @client = client || OpenAI::Client.new(access_token: api_key)
end

Instance Method Details

#bind_tools(tools) ⇒ Object 



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# File 'lib/langgraph_rb/chat_ruby_openai.rb', line 16

def bind_tools(tools)
  dup_instance = self.class.new(model: @model, temperature: @temperature)
  dup_instance.instance_variable_set(:@client, @client)
  dup_instance.instance_variable_set(:@bound_tools, Array(tools))
  dup_instance
end

#call(messages, tools: nil) ⇒ Object 



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# File 'lib/langgraph_rb/chat_ruby_openai.rb', line 23

def call(messages, tools: nil)
  raise ArgumentError, 'messages must be an Array' unless messages.is_a?(Array)

  tool_definitions = (tools || @bound_tools)
  tool_schemas = Array(tool_definitions).flat_map do |tool|
    if tool.respond_to?(:to_openai_tool_schema)
      Array(tool.to_openai_tool_schema)
    else
      [tool]
    end
  end

  request_payload = {
    model: @model,       
    messages: normalize_messages(messages)
  }

  request_payload[:temperature] = @temperature if @temperature.present?

  if tool_schemas && !tool_schemas.empty?
    request_payload[:tools] = tool_schemas
    request_payload[:tool_choice] = 'auto'
  end

  notify_llm_request({
    name: 'OpenAI::ChatCompletion',
    model: @model,
    model_parameters: { temperature: @temperature },
    input: request_payload
  })

  # ruby-openai 8.1.x: client.chat(parameters: {...}) returns a Hash
  response = @client.chat(parameters: request_payload)

  message = extract_message_from_response(response)
  tool_calls = message["tool_calls"]
  text_content = message["content"]

  usage = extract_usage_from_response(response)
  notify_llm_response({
    output: tool_calls ? { tool_calls: tool_calls } : text_content,
    prompt_tokens: usage[:prompt_tokens],
    completion_tokens: usage[:completion_tokens],
    total_tokens: usage[:total_tokens]
  })

  if tool_calls && !tool_calls.empty?
    normalized_calls = tool_calls.map do |tc|
      {
        id: tc["id"],
        name: tc["function"]["name"],
        arguments: parse_tool_arguments(tc["function"]["arguments"])
      }
    end
    { tool_calls: normalized_calls }
  else
    text_content
  end
rescue => e
  notify_llm_error({ error: e.response&.dig(:body) || e.message })
  raise e
end