Class: RailsMcpEngine::ChatController

Inherits:
ApplicationController show all
Defined in:
app/controllers/rails_mcp_engine/chat_controller.rb

Instance Method Summary collapse

Instance Method Details

#send_message ⇒ Object 



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# File 'app/controllers/rails_mcp_engine/chat_controller.rb', line 26

def send_message
  user_message = params[:message].to_s
  model = params[:model].to_s
  conversation_history = JSON.parse(params[:conversation_history] || '[]', symbolize_names: true)

  if user_message.strip.empty?
    render json: { error: 'Message is required' }, status: :bad_request
    return
  end

  provider = if model.start_with?('gemini')
               :gemini
             elsif model.start_with?('claude')
               :anthropic
             else
               :openai
             end

  # Create RubyLLM chat instance with configuration
  chat = RubyLLM.chat(
    provider: provider,
    model: model
  )

  # Register all available tools
  tool_classes = get_tool_classes
  chat = tool_classes.reduce(chat) { |c, tool_class| c.with_tool(tool_class) }

  # Prepare the message with conversation history context
  if conversation_history.empty?
    # First message: just send as-is
    full_message = user_message
  else
    # Include conversation history for context
    context_parts = ['Previous conversation:']
    conversation_history.each do |msg|
      role_label = msg[:role] == 'user' ? 'User' : 'Assistant'
      context_parts << "#{role_label}: #{msg[:content]}"
    end
    context_parts << "\nCurrent question:"
    context_parts << user_message
    full_message = context_parts.join("\n\n")
  end

  # Ask the question and capture response
  begin
    # Ruby LLM handles tool calling automatically
    response = chat.ask(full_message)
    assistant_content = response.content

    # Build conversation history manually since Ruby LLM manages it internally
    # Add user message
    conversation_history << { role: 'user', content: user_message }
    # Add assistant response
    conversation_history << { role: 'assistant', content: assistant_content }

    # Tool results are handled transparently by Ruby LLM
    # We don't have direct access to them, so return empty array
    tool_results = []

    render json: {
      conversation_history: conversation_history,
      tool_results: tool_results
    }
  rescue StandardError => e
    render json: { error: e.message }, status: :bad_request
  end
end

#show ⇒ Object 



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# File 'app/controllers/rails_mcp_engine/chat_controller.rb', line 7

def show
  @tools = schemas
  @models = [
    # OpenAI
    'gpt-5-nano',
    'gpt-4.1',
    'gpt-4o',
    'gpt-4o-mini',
    # Google
    'gemini-2.5-pro',
    'gemini-2.0-pro-exp',
    # Anthropic
    'claude-sonnet-4-5',
    'claude-3-7-sonnet-20250219',
    'claude-3-5-haiku-20241022',
    'claude-3-haiku-20240307'
  ]
end