Class: AIA::RubyLLMAdapter

Inherits:
Object
  • Object
show all
Defined in:
lib/aia/ruby_llm_adapter.rb

Defined Under Namespace

Classes: MultiModelResponse

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize ⇒ RubyLLMAdapter

Returns a new instance of RubyLLMAdapter.



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# File 'lib/aia/ruby_llm_adapter.rb', line 10

def initialize
  @model_specs = extract_models_config  # Full specs with role info
  @models = extract_model_names(@model_specs)  # Just model names for backward compat
  @chats = {}
  @contexts = {} # Store isolated contexts for each model

  configure_rubyllm
  refresh_local_model_registry
  setup_chats_with_tools
end

Dynamic Method Handling

This class handles dynamic methods through the method_missing method

#method_missing(method, *args, &block) ⇒ Object 



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# File 'lib/aia/ruby_llm_adapter.rb', line 624

def method_missing(method, *args, &block)
  # Use the first chat instance for backward compatibility with method_missing
  first_chat = @chats.values.first
  if first_chat&.respond_to?(method)
    first_chat.public_send(method, *args, &block)
  else
    super
  end
end

Instance Attribute Details

#chats ⇒ Object (readonly)

Returns the value of attribute chats.



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# File 'lib/aia/ruby_llm_adapter.rb', line 8

def chats
  @chats
end

#model_specs ⇒ Object (readonly)

Returns the value of attribute model_specs.



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# File 'lib/aia/ruby_llm_adapter.rb', line 8

def model_specs
  @model_specs
end

#tools ⇒ Object (readonly)

Returns the value of attribute tools.



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# File 'lib/aia/ruby_llm_adapter.rb', line 8

def tools
  @tools
end

Instance Method Details

#build_consensus_prompt(results) ⇒ Object 



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# File 'lib/aia/ruby_llm_adapter.rb', line 437

def build_consensus_prompt(results)
  prompt_parts = []
  prompt_parts << "You are tasked with creating a consensus response based on multiple AI model responses to the same query."
  prompt_parts << "Please analyze the following responses and provide a unified, comprehensive answer that:"
  prompt_parts << "- Incorporates the best insights from all models"
  prompt_parts << "- Resolves any contradictions with clear reasoning"
  prompt_parts << "- Provides additional context or clarification when helpful"
  prompt_parts << "- Maintains accuracy and avoids speculation"
  prompt_parts << ""
  prompt_parts << "Model responses:"
  prompt_parts << ""

  results.each do |model_name, result|
    # Extract content from RubyLLM::Message if needed
    content = if result.respond_to?(:content)
                result.content
              else
                result.to_s
              end
    next if content.start_with?("Error with")
    prompt_parts << "#{model_name}:"
    prompt_parts << content
    prompt_parts << ""
  end

  prompt_parts << "Please provide your consensus response:"
  prompt_parts.join("\n")
end

#chat(prompt) ⇒ Object 



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# File 'lib/aia/ruby_llm_adapter.rb', line 287

def chat(prompt)
  result = if @models.size == 1
    # Single model - use the original behavior
    single_model_chat(prompt, @models.first)
  else
    # Multiple models - use concurrent processing
    multi_model_chat(prompt)
  end

  result
end

#clear_context ⇒ Object

Clear the chat context/history Needed for the //clear and //restore directives Simplified with ADR-002: Each model has isolated context, no global state to manage



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# File 'lib/aia/ruby_llm_adapter.rb', line 582

def clear_context
  old_chats = @chats.dup
  new_chats = {}

  @models.each do |model_name|
    begin
      # Get the isolated context for this model
      context = @contexts[model_name]
      actual_model, provider = extract_model_and_provider(model_name)

      # Create a fresh chat instance from the same isolated context
      chat = if provider
               context.chat(model: actual_model, provider: provider, assume_model_exists: true)
             else
               context.chat(model: actual_model)
             end

      # Re-add tools if they were previously loaded
      if @tools && !@tools.empty? && chat.model&.supports_functions?
        chat.with_tools(*@tools)
      end

      new_chats[model_name] = chat
    rescue StandardError => e
      # If recreation fails, keep the old chat but clear its messages
      warn "Warning: Could not recreate chat for #{model_name}: #{e.message}. Clearing existing chat."
      chat = old_chats[model_name]
      if chat&.instance_variable_defined?(:@messages)
        chat.instance_variable_set(:@messages, [])
      end
      chat.clear_history if chat&.respond_to?(:clear_history)
      new_chats[model_name] = chat
    end
  end

  @chats = new_chats
  'Chat context successfully cleared.'
rescue StandardError => e
  "Error clearing chat context: #{e.message}"
end

#configure_rubyllm ⇒ Object 



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# File 'lib/aia/ruby_llm_adapter.rb', line 22

def configure_rubyllm
  # TODO: Add some of these configuration items to AIA.config
  RubyLLM.configure do |config|
    config.anthropic_api_key  = ENV.fetch('ANTHROPIC_API_KEY', nil)
    config.deepseek_api_key   = ENV.fetch('DEEPSEEK_API_KEY', nil)
    config.gemini_api_key     = ENV.fetch('GEMINI_API_KEY', nil)
    config.gpustack_api_key   = ENV.fetch('GPUSTACK_API_KEY', nil)
    config.mistral_api_key    = ENV.fetch('MISTRAL_API_KEY', nil)
    config.openrouter_api_key = ENV.fetch('OPENROUTER_API_KEY', nil)
    config.perplexity_api_key = ENV.fetch('PERPLEXITY_API_KEY', nil)

    # These providers require a little something extra
    config.openai_api_key         = ENV.fetch('OPENAI_API_KEY', nil)
    config.openai_organization_id = ENV.fetch('OPENAI_ORGANIZATION_ID', nil)
    config.openai_project_id      = ENV.fetch('OPENAI_PROJECT_ID', nil)

    config.bedrock_api_key       = ENV.fetch('BEDROCK_ACCESS_KEY_ID', nil)
    config.bedrock_secret_key    = ENV.fetch('BEDROCK_SECRET_ACCESS_KEY', nil)
    config.bedrock_region        = ENV.fetch('BEDROCK_REGION', nil)
    config.bedrock_session_token = ENV.fetch('BEDROCK_SESSION_TOKEN', nil)

    # Ollama is based upon the OpenAI API so it needs to over-ride a few things
    config.ollama_api_base = ENV.fetch('OLLAMA_API_BASE', nil)

    # --- Custom OpenAI Endpoint ---
    # Use this for Azure OpenAI, proxies, or self-hosted models via OpenAI-compatible APIs.
    # For osaurus: Use model name prefix "osaurus/" and set OSAURUS_API_BASE env var
    # For LM Studio: Use model name prefix "lms/" and set LMS_API_BASE env var
    config.openai_api_base = ENV.fetch('OPENAI_API_BASE', nil) # e.g., "https://your-azure.openai.azure.com"

    # --- Default Models ---
    # Used by RubyLLM.chat, RubyLLM.embed, RubyLLM.paint if no model is specified.
    # config.default_model            = 'gpt-4.1-nano'            # Default: 'gpt-4.1-nano'
    # config.default_embedding_model  = 'text-embedding-3-small'  # Default: 'text-embedding-3-small'
    # config.default_image_model      = 'dall-e-3'                # Default: 'dall-e-3'

    # --- Connection Settings ---
    config.request_timeout            = 120 # Request timeout in seconds (default: 120)
            config.max_retries                = 3   # Max retries on transient network errors (default: 3)
            config.retry_interval             = 0.1 # Initial delay in seconds (default: 0.1)
            config.retry_backoff_factor       = 2   # Multiplier for subsequent retries (default: 2)
            config.retry_interval_randomness  = 0.5 # Jitter factor (default: 0.5)

    # Connection pooling settings removed - not supported in current RubyLLM version
    # config.connection_pool_size       = 10  # Number of connections to maintain in pool
    # config.connection_pool_timeout    = 60  # Connection pool timeout in seconds
    # config.log_file   = '/logs/ruby_llm.log'
    config.log_level = :fatal # debug level can also be set to debug by setting RUBYLLM_DEBUG envar to true
  end
end

#create_isolated_context_for_model(model_name) ⇒ Object

Create an isolated RubyLLM::Context for a model to prevent cross-talk (ADR-002) Each model gets its own context with provider-specific configuration



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# File 'lib/aia/ruby_llm_adapter.rb', line 87

def create_isolated_context_for_model(model_name)
  config = RubyLLM.config.dup

  # Apply provider-specific configuration
  if model_name.start_with?('lms/')
    config.openai_api_base = ENV.fetch('LMS_API_BASE', 'http://localhost:1234/v1')
    config.openai_api_key = 'dummy' # Local servers don't need a real API key
  elsif model_name.start_with?('osaurus/')
    config.openai_api_base = ENV.fetch('OSAURUS_API_BASE', 'http://localhost:11434/v1')
    config.openai_api_key = 'dummy' # Local servers don't need a real API key
  end

  RubyLLM::Context.new(config)
end

#drop_duplicate_tools ⇒ Object 



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# File 'lib/aia/ruby_llm_adapter.rb', line 267

def drop_duplicate_tools
  seen_names = Set.new
  original_size = @tools.size

  @tools.select! do |tool|
    tool_name = tool.name
    if seen_names.include?(tool_name)
      warn "WARNING: Duplicate tool name detected: '#{tool_name}'. Only the first occurrence will be used."
      false
    else
      seen_names.add(tool_name)
      true
    end
  end

  removed_count = original_size - @tools.size
  warn "Removed #{removed_count} duplicate tools" if removed_count > 0
end

#extract_model_and_provider(model_name) ⇒ Object

Extract the actual model name and provider from the prefixed model_name Returns: [actual_model, provider] where provider may be nil for auto-detection



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# File 'lib/aia/ruby_llm_adapter.rb', line 105

def extract_model_and_provider(model_name)
  if model_name.start_with?('ollama/')
    [model_name.sub('ollama/', ''), 'ollama']
  elsif model_name.start_with?('lms/') || model_name.start_with?('osaurus/')
    [model_name.sub(%r{^(lms|osaurus)/}, ''), 'openai']
  else
    [model_name, nil] # Let RubyLLM auto-detect provider
  end
end

#format_individual_responses(results) ⇒ Object 



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# File 'lib/aia/ruby_llm_adapter.rb', line 466

def format_individual_responses(results)
  # For metrics support, return a special structure if all results have token info
  has_metrics = results.values.all? { |r| r.respond_to?(:input_tokens) && r.respond_to?(:output_tokens) }

  if has_metrics && AIA.config.show_metrics
    # Return structured data that preserves metrics for multi-model
    format_multi_model_with_metrics(results)
  else
    # Original string formatting for non-metrics mode with role labels (ADR-005)
    output = []
    results.each do |internal_id, result|
      # Get model spec to include role in output
      spec = get_model_spec(internal_id)
      display_name = format_model_display_name(spec)

      output << "from: #{display_name}"
      # Extract content from RubyLLM::Message if needed
      content = if result.respond_to?(:content)
                  result.content
                else
                  result.to_s
                end
      output << content
      output << "" # Add blank line between results
    end
    output.join("\n")
  end
end

#format_model_display_name(spec) ⇒ Object

Format display name with instance number and role (ADR-005)



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# File 'lib/aia/ruby_llm_adapter.rb', line 496

def format_model_display_name(spec)
  return spec unless spec.is_a?(Hash)

  model_name = spec[:model]
  instance = spec[:instance]
  role = spec[:role]

  # Add instance number if > 1
  display = if instance > 1
              "#{model_name} ##{instance}"
            else
              model_name
            end

  # Add role label if present
  display += " (#{role})" if role

  display
end

#format_multi_model_results(results) ⇒ Object 



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# File 'lib/aia/ruby_llm_adapter.rb', line 398

def format_multi_model_results(results)
  use_consensus = should_use_consensus_mode?

  if use_consensus
    # Generate consensus response using primary model
    generate_consensus_response(results)
  else
    # Show individual responses from all models
    format_individual_responses(results)
  end
end

#format_multi_model_with_metrics(results) ⇒ Object 



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# File 'lib/aia/ruby_llm_adapter.rb', line 516

def format_multi_model_with_metrics(results)
  # Create a composite response that includes all model responses and metrics
  formatted_content = []
  metrics_data = []

  results.each do |model_name, result|
    formatted_content << "from: #{model_name}"
    formatted_content << result.content
    formatted_content << ""

    # Collect metrics for each model
    metrics_data << {
      model_id: model_name,
      input_tokens: result.input_tokens,
      output_tokens: result.output_tokens
    }
  end

  # Return a special MultiModelResponse that ChatProcessorService can handle
  MultiModelResponse.new(formatted_content.join("\n"), metrics_data)
end

#generate_consensus_response(results) ⇒ Object 



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# File 'lib/aia/ruby_llm_adapter.rb', line 415

def generate_consensus_response(results)
  primary_model = @models.first
  primary_chat = @chats[primary_model]

  # Build the consensus prompt with all model responses
  # Note: This prompt does NOT include the model's role (ADR-005)
  # The primary model synthesizes neutrally without role bias
  consensus_prompt = build_consensus_prompt(results)

  begin
    # Have the primary model generate the consensus
    # The consensus prompt is already role-neutral
    consensus_result = primary_chat.ask(consensus_prompt).content

    # Format the consensus response - no role label for consensus
    "from: #{primary_model}\n#{consensus_result}"
  rescue StandardError => e
    # If consensus fails, fall back to individual responses
    "Error generating consensus: #{e.message}\n\n" + format_individual_responses(results)
  end
end

#load_tools ⇒ Object 



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# File 'lib/aia/ruby_llm_adapter.rb', line 211

def load_tools
  @tools = []

  support_local_tools
  support_mcp
  filter_tools_by_allowed_list
  filter_tools_by_rejected_list
  drop_duplicate_tools

  if tools.empty?
    AIA.config.tool_names = ''
  else
    AIA.config.tool_names = @tools.map(&:name).join(', ')
    AIA.config.tools      = @tools
  end
end

#load_tools_lazy_mcp_support_only_when_needed ⇒ Object 



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# File 'lib/aia/ruby_llm_adapter.rb', line 193

def load_tools_lazy_mcp_support_only_when_needed
  @tools = []

  support_local_tools
  support_mcp_lazy
  filter_tools_by_allowed_list
  filter_tools_by_rejected_list
  drop_duplicate_tools

  if tools.empty?
    AIA.config.tool_names = ''
  else
    AIA.config.tool_names = @tools.map(&:name).join(', ')
    AIA.config.tools      = @tools
  end
end

#multi_model_chat(prompt_or_contexts) ⇒ Object 



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# File 'lib/aia/ruby_llm_adapter.rb', line 364

def multi_model_chat(prompt_or_contexts)
  results = {}

  # Check if we're receiving per-model contexts (Hash) or shared prompt (String/Array) - ADR-002 revised
  per_model_contexts = prompt_or_contexts.is_a?(Hash) &&
                       prompt_or_contexts.keys.all? { |k| @models.include?(k) }

  Async do |task|
    @models.each do |internal_id|
      task.async do
        begin
          # Use model-specific context if available, otherwise shared prompt
          prompt = if per_model_contexts
                     prompt_or_contexts[internal_id]
                   else
                     prompt_or_contexts
                   end

          # Add per-model role if specified (ADR-005)
          prompt = prepend_model_role(prompt, internal_id)

          result = single_model_chat(prompt, internal_id)
          results[internal_id] = result
        rescue StandardError => e
          results[internal_id] = "Error with #{internal_id}: #{e.message}"
        end
      end
    end
  end

  # Format and return results from all models
  format_multi_model_results(results)
end

#prepend_model_role(prompt, internal_id) ⇒ Object

Prepend role content to prompt for a specific model (ADR-005)



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# File 'lib/aia/ruby_llm_adapter.rb', line 323

def prepend_model_role(prompt, internal_id)
  # Get model spec to find role
  spec = get_model_spec(internal_id)
  return prompt unless spec && spec[:role]

  # Get role content using PromptHandler
  # Need to create PromptHandler instance if not already available
  prompt_handler = AIA::PromptHandler.new
  role_content = prompt_handler.load_role_for_model(spec, AIA.config.role)

  return prompt unless role_content

  # Prepend role to prompt based on prompt type
  if prompt.is_a?(String)
    # Simple string prompt
    "#{role_content}\n\n#{prompt}"
  elsif prompt.is_a?(Array)
    # Conversation array - prepend to first user message
    prepend_role_to_conversation(prompt, role_content)
  else
    prompt
  end
end

#prepend_role_to_conversation(conversation, role_content) ⇒ Object 



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# File 'lib/aia/ruby_llm_adapter.rb', line 347

def prepend_role_to_conversation(conversation, role_content)
  # Find the first user message and prepend role
  modified = conversation.dup
  first_user_index = modified.find_index { |msg| msg[:role] == "user" || msg["role"] == "user" }

  if first_user_index
    msg = modified[first_user_index].dup
    role_key = msg.key?(:role) ? :role : "role"
    content_key = msg.key?(:content) ? :content : "content"

    msg[content_key] = "#{role_content}\n\n#{msg[content_key]}"
    modified[first_user_index] = msg
  end

  modified
end

#refresh_local_model_registry ⇒ Object 



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# File 'lib/aia/ruby_llm_adapter.rb', line 74

def refresh_local_model_registry
  if  AIA.config.refresh.nil?           ||
      Integer(AIA.config.refresh).zero? ||
      Date.today > (AIA.config.last_refresh + Integer(AIA.config.refresh))
    RubyLLM.models.refresh!
    AIA.config.last_refresh = Date.today
    AIA::Config.dump_config(AIA.config, AIA.config.config_file) if AIA.config.config_file
  end
end

#respond_to_missing?(method, include_private = false) ⇒ Boolean

Returns:

  • (Boolean) 


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# File 'lib/aia/ruby_llm_adapter.rb', line 635

def respond_to_missing?(method, include_private = false)
  # Check if any of our chat instances respond to the method
  @chats.values.any? { |chat| chat.respond_to?(method) } || super
end

#setup_chats_with_tools ⇒ Object 



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# File 'lib/aia/ruby_llm_adapter.rb', line 116

def setup_chats_with_tools
  valid_chats = {}
  valid_contexts = {}
  valid_specs = []
  failed_models = []

  @model_specs.each do |spec|
    model_name = spec[:model]          # Actual model name (e.g., "gpt-4o")
    internal_id = spec[:internal_id]   # Key for storage (e.g., "gpt-4o#1", "gpt-4o#2")

    begin
      # Create isolated context for this model to prevent cross-talk (ADR-002)
      context = create_isolated_context_for_model(model_name)

      # Determine provider and actual model name
      actual_model, provider = extract_model_and_provider(model_name)

      # Validate LM Studio models
      if model_name.start_with?('lms/')
        lms_api_base = ENV.fetch('LMS_API_BASE', 'http://localhost:1234/v1')
        validate_lms_model!(actual_model, lms_api_base)
      end

      # Create chat using isolated context
      chat = if provider
               context.chat(model: actual_model, provider: provider, assume_model_exists: true)
             else
               context.chat(model: actual_model)
             end

      valid_chats[internal_id] = chat
      valid_contexts[internal_id] = context
      valid_specs << spec
    rescue StandardError => e
      failed_models << "#{internal_id}: #{e.message}"
    end
  end

  # Report failed models but continue with valid ones
  unless failed_models.empty?
    puts "\n❌ Failed to initialize the following models:"
    failed_models.each { |failure| puts "   - #{failure}" }
  end

  # If no models initialized successfully, exit
  if valid_chats.empty?
    puts "\n❌ No valid models could be initialized. Exiting."
    puts "\n💡 Available models can be listed with: bin/aia --help models"
    exit 1
  end

  @chats = valid_chats
  @contexts = valid_contexts
  @model_specs = valid_specs
  @models = valid_chats.keys

  # Update the config to reflect only the valid models (keep as specs)
  AIA.config.model = @model_specs

  # Report successful models
  if failed_models.any?
    puts "\n✅ Successfully initialized: #{@models.join(', ')}"
    puts
  end

  # Use the first chat to determine tool support (assuming all models have similar tool support)
  first_chat = @chats.values.first
  return unless first_chat&.model&.supports_functions?

  load_tools_lazy_mcp_support_only_when_needed

  @chats.each_value do |chat|
    chat.with_tools(*tools) unless tools.empty?
  end
end

#should_use_consensus_mode? ⇒ Boolean

Returns:

  • (Boolean) 


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# File 'lib/aia/ruby_llm_adapter.rb', line 410

def should_use_consensus_mode?
  # Only use consensus when explicitly enabled with --consensus flag
  AIA.config.consensus == true
end

#single_model_chat(prompt, internal_id) ⇒ Object 



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# File 'lib/aia/ruby_llm_adapter.rb', line 299

def single_model_chat(prompt, internal_id)
  chat_instance = @chats[internal_id]
  modes = chat_instance.model.modalities

  # TODO: Need to consider how to handle multi-mode models
  result = if modes.text_to_text?
    text_to_text_single(prompt, internal_id)
  elsif modes.image_to_text?
    image_to_text_single(prompt, internal_id)
  elsif modes.text_to_image?
    text_to_image_single(prompt, internal_id)
  elsif modes.text_to_audio?
    text_to_audio_single(prompt, internal_id)
  elsif modes.audio_to_text?
    audio_to_text_single(prompt, internal_id)
  else
    # TODO: what else can be done?
    "Error: No matching modality for model #{internal_id}"
  end

  result
end

#speak(_text) ⇒ Object 



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# File 'lib/aia/ruby_llm_adapter.rb', line 560

def speak(_text)
  output_file = "#{Time.now.to_i}.mp3"

  # NOTE: RubyLLM doesn't have a direct text-to-speech feature
  # This is a placeholder for a custom implementation or external service
  begin
    # Try using a TTS API if available
    # For now, we'll use a mock implementation
    File.write(output_file, 'Mock TTS audio content')
    if File.exist?(output_file) && system("which #{AIA.config.speak_command} > /dev/null 2>&1")
      system("#{AIA.config.speak_command} #{output_file}")
    end
    "Audio generated and saved to: #{output_file}"
  rescue StandardError => e
    "Error generating audio: #{e.message}"
  end
end

#support_local_tools ⇒ Object 



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# File 'lib/aia/ruby_llm_adapter.rb', line 229

def support_local_tools
  @tools += ObjectSpace.each_object(Class).select do |klass|
    next false unless klass < RubyLLM::Tool

    # Filter out tools that can't be instantiated without arguments
    # RubyLLM calls tool.new without args, so we must verify each tool works
    begin
      klass.new
      true
    rescue ArgumentError, LoadError, StandardError
      # Skip tools that require arguments or have missing dependencies
      false
    end
  end
end

#support_mcp ⇒ Object 



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# File 'lib/aia/ruby_llm_adapter.rb', line 259

def support_mcp
  RubyLLM::MCP.establish_connection
  @tools += RubyLLM::MCP.tools
rescue StandardError => e
  warn "Warning: Failed to connect MCP clients: #{e.message}"
end

#support_mcp_lazy ⇒ Object 



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# File 'lib/aia/ruby_llm_adapter.rb', line 246

def support_mcp_lazy
  # Only load MCP tools if MCP servers are actually configured
  return if AIA.config.mcp_servers.nil? || AIA.config.mcp_servers.empty?

  begin
    RubyLLM::MCP.establish_connection
    @tools += RubyLLM::MCP.tools
  rescue StandardError => e
    warn "Warning: Failed to connect MCP clients: #{e.message}"
  end
end

#transcribe(audio_file) ⇒ Object 



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# File 'lib/aia/ruby_llm_adapter.rb', line 553

def transcribe(audio_file)
  # Use the first model for transcription
  first_model = @models.first
  @chats[first_model].ask('Transcribe this audio', with: audio_file).content
end