object

defmodule Object.FunctionCalling do
  @moduledoc """
  LLM-powered function calling system for Object self-organization.
  
  This module enables Objects to:
  1. Dynamically discover and call functions on other Objects
  2. Use LLM reasoning to select appropriate functions and parameters
  3. Compose complex workflows through chained function calls
  4. Adapt function calling strategies based on outcomes
  5. Self-organize through coordinated function execution
  
  The system treats each Object method as a callable function that can be
  invoked remotely with LLM-generated parameters and context.
  """
  
  alias Object.{LLMIntegration, InteractionPatterns, MetaDSL}
  
  defstruct [
    :object_registry,
    :function_catalog,
    :execution_history,
    :adaptation_policies
  ]
  
  @type t :: %__MODULE__{
    object_registry: map(),
    function_catalog: map(),
    execution_history: [execution_record()],
    adaptation_policies: [policy()]
  }
  
  @type execution_record :: %{
    function_id: String.t(),
    caller_object: String.t(),
    target_object: String.t(),
    parameters: map(),
    result: any(),
    success: boolean(),
    timestamp: DateTime.t(),
    reasoning_chain: [String.t()]
  }
  
  @type policy :: %{
    condition: function(),
    adaptation: atom(),
    threshold: float()
  }
  
  @doc """
  Initializes the function calling system.
  """
  def new(_opts \\ []) do
    %__MODULE__{
      object_registry: %{},
      function_catalog: initialize_function_catalog(),
      execution_history: [],
      adaptation_policies: initialize_adaptation_policies()
    }
  end
  
  @doc """
  Registers an Object and its callable functions in the system.
  """
  def register_object(%__MODULE__{} = system, object) do
    # Extract callable functions from object
    callable_functions = extract_callable_functions(object)
    
    # Update registry
    updated_registry = Map.put(system.object_registry, object.id, %{
      object: object,
      functions: callable_functions,
      registered_at: DateTime.utc_now(),
      availability: :online
    })
    
    # Update function catalog
    updated_catalog = update_function_catalog(system.function_catalog, object.id, callable_functions)
    
    %{system |
      object_registry: updated_registry,
      function_catalog: updated_catalog
    }
  end
  
  @doc """
  Executes a function call using LLM reasoning to determine parameters.
  """
  def execute_llm_function_call(%__MODULE__{} = system, caller_object, target_function, intent, context \\ %{}) do
    case find_function_implementation(system, target_function) do
      {:ok, target_object_id, function_spec} ->
        # Use LLM to generate appropriate parameters
        parameter_generation_result = generate_function_parameters(
          caller_object,
          function_spec,
          intent,
          context
        )
        
        case parameter_generation_result do
          {:ok, parameters, reasoning_chain} ->
            # Execute the function call
            execution_result = execute_remote_function_call(
              system,
              caller_object,
              target_object_id,
              target_function,
              parameters
            )
            
            # Record execution
            execution_record = %{
              function_id: "#{target_object_id}.#{target_function}",
              caller_object: caller_object.id,
              target_object: target_object_id,
              parameters: parameters,
              result: execution_result,
              success: match?({:ok, _}, execution_result),
              timestamp: DateTime.utc_now(),
              reasoning_chain: reasoning_chain
            }
            
            updated_system = record_execution(system, execution_record)
            
            # Apply adaptation policies
            {final_system, adaptations} = apply_adaptation_policies(updated_system, execution_record)
            
            {:ok, execution_result, final_system, adaptations}
            
          {:error, reason} ->
            {:error, {:parameter_generation_failed, reason}}
        end
        
      {:error, reason} ->
        {:error, reason}
    end
  end
  
  @doc """
  Discovers and suggests optimal function calls for achieving a goal.
  """
  def discover_function_composition(%__MODULE__{} = system, caller_object, goal_description, constraints \\ []) do
    # Use LLM to analyze available functions and compose a workflow
    composition_prompt = %{
      goal: goal_description,
      available_functions: get_available_functions(system, caller_object),
      caller_capabilities: caller_object.methods,
      caller_state: caller_object.state,
      constraints: constraints,
      execution_history: get_relevant_execution_history(system, goal_description)
    }
    
    case LLMIntegration.reason_about_goal(
      caller_object,
      "Discover optimal function composition to achieve: #{goal_description}",
      composition_prompt
    ) do
      {:ok, reasoning_result, updated_caller} ->
        # Parse the suggested function composition
        composition = parse_function_composition(reasoning_result)
        
        {:ok, composition, updated_caller}
        
      {:error, reason} ->
        {:error, reason}
    end
  end
  
  @doc """
  Executes a composed workflow of function calls with dependency management.
  """
  def execute_function_composition(%__MODULE__{} = system, caller_object, composition) do
    execution_plan = create_execution_plan(composition)
    
    # Execute functions in dependency order
    {final_system, execution_results} = Enum.reduce(execution_plan.stages, {system, []}, fn stage, {acc_system, acc_results} ->
      stage_results = execute_parallel_stage(acc_system, caller_object, stage)
      {stage_results.updated_system, acc_results ++ stage_results.results}
    end)
    
    workflow_result = %{
      composition_id: composition.id,
      stages_executed: length(execution_plan.stages),
      total_functions_called: length(execution_results),
      success_rate: calculate_success_rate(execution_results),
      final_result: aggregate_stage_results(execution_results),
      execution_time: calculate_total_execution_time(execution_results)
    }
    
    {:ok, workflow_result, final_system}
  end
  
  @doc """
  Enables collaborative function calling between multiple Objects.
  """
  def collaborative_function_execution(%__MODULE__{} = system, participating_objects, shared_goal, coordination_strategy \\ :consensus) do
    # Use interaction patterns to coordinate the collaboration
    case InteractionPatterns.initiate_pattern(
      coordination_strategy,
      hd(participating_objects),
      tl(participating_objects),
      %{objective: shared_goal, type: :function_collaboration}
    ) do
      {:ok, coordination_result} ->
        # Execute the collaborative function calls based on coordination outcome
        collaborative_execution = execute_collaborative_functions(
          system,
          participating_objects,
          coordination_result
        )
        
        {:ok, collaborative_execution}
        
      {:error, reason} ->
        {:error, reason}
    end
  end
  
  @doc """
  Adapts function calling strategies based on execution outcomes.
  """
  def adapt_execution_strategy(%__MODULE__{} = _system, caller_object, performance_metrics) do
    # Use meta-DSL to refine function calling approach
    case MetaDSL.execute(
      caller_object.meta_dsl,
      :refine,
      caller_object,
      {:function_calling_strategy, performance_metrics}
    ) do
      {:ok, refinement_result, updated_meta_dsl} ->
        updated_caller = %{caller_object | meta_dsl: updated_meta_dsl}
        
        adaptation_result = %{
          strategy_updated: true,
          refinements: refinement_result,
          performance_improvement: estimate_performance_improvement(refinement_result)
        }
        
        {:ok, adaptation_result, updated_caller}
        
      {:error, reason} ->
        {:error, reason}
    end
  end
  
  # Private implementation functions
  
  defp initialize_function_catalog do
    %{
      by_category: %{
        coordination: [],
        computation: [],
        communication: [],
        learning: [],
        adaptation: []
      },
      by_object_type: %{},
      metadata: %{
        total_functions: 0,
        last_updated: DateTime.utc_now()
      }
    }
  end
  
  defp initialize_adaptation_policies do
    [
      %{
        condition: fn record -> not record.success end,
        adaptation: :retry_with_modified_parameters,
        threshold: 0.3
      },
      %{
        condition: fn record -> record.success and execution_time_acceptable?(record) end,
        adaptation: :cache_successful_pattern,
        threshold: 0.8
      },
      %{
        condition: fn record -> frequent_failures?(record) end,
        adaptation: :suggest_alternative_function,
        threshold: 0.5
      }
    ]
  end
  
  defp extract_callable_functions(object) do
    for method <- object.methods do
      %{
        name: method,
        object_id: object.id,
        description: generate_function_description(object, method),
        parameters: infer_function_parameters(object, method),
        return_type: infer_return_type(object, method),
        category: categorize_function(method),
        availability: :available
      }
    end
  end
  
  defp update_function_catalog(catalog, object_id, functions) do
    # Update by category
    updated_by_category = Enum.reduce(functions, catalog.by_category, fn func, acc ->
      category_functions = Map.get(acc, func.category, [])
      Map.put(acc, func.category, [func | category_functions])
    end)
    
    # Update by object type
    updated_by_object_type = Map.put(catalog.by_object_type, object_id, functions)
    
    # Update metadata
    updated_metadata = %{
      total_functions: catalog.metadata.total_functions + length(functions),
      last_updated: DateTime.utc_now()
    }
    
    %{catalog |
      by_category: updated_by_category,
      by_object_type: updated_by_object_type,
      metadata: updated_metadata
    }
  end
  
  defp find_function_implementation(system, function_name) do
    # Search through function catalog
    all_functions = system.function_catalog.by_object_type
                   |> Map.values()
                   |> List.flatten()
    
    case Enum.find(all_functions, fn func -> func.name == function_name end) do
      nil -> {:error, {:function_not_found, function_name}}
      function_spec -> {:ok, function_spec.object_id, function_spec}
    end
  end
  
  defp generate_function_parameters(caller_object, function_spec, intent, context) do
    # Use LLM to generate appropriate parameters
    parameter_prompt = %{
      function_name: function_spec.name,
      function_description: function_spec.description,
      expected_parameters: function_spec.parameters,
      caller_intent: intent,
      context: context,
      caller_state: caller_object.state
    }
    
    case LLMIntegration.reason_about_goal(
      caller_object,
      "Generate appropriate parameters for function call",
      parameter_prompt
    ) do
      {:ok, reasoning_result, _} ->
        parameters = parse_generated_parameters(reasoning_result)
        {:ok, parameters, reasoning_result.reasoning_chain}
        
      {:error, reason} ->
        {:error, reason}
    end
  end
  
  defp execute_remote_function_call(system, _caller_object, target_object_id, function_name, parameters) do
    case Map.get(system.object_registry, target_object_id) do
      nil ->
        {:error, {:target_object_not_found, target_object_id}}
      
      %{object: target_object, availability: :online} ->
        # Execute the method on the target object
        case Object.execute_method(target_object, function_name, [parameters]) do
          {:ok, result} -> {:ok, result}
          {:error, reason} -> {:error, reason}
          result -> {:ok, result}  # Handle direct returns
        end
        
      %{availability: status} ->
        {:error, {:target_object_unavailable, status}}
    end
  end
  
  defp record_execution(system, execution_record) do
    updated_history = [execution_record | system.execution_history]
    %{system | execution_history: updated_history}
  end
  
  defp apply_adaptation_policies(system, execution_record) do
    applicable_policies = Enum.filter(system.adaptation_policies, fn policy ->
      policy.condition.(execution_record)
    end)
    
    adaptations = for policy <- applicable_policies do
      apply_adaptation(system, execution_record, policy.adaptation)
    end
    
    {system, adaptations}
  end
  
  defp get_available_functions(system, caller_object) do
    # Filter functions based on caller's access permissions and capabilities
    all_functions = system.function_catalog.by_category
                   |> Map.values()
                   |> List.flatten()
    
    accessible_functions = Enum.filter(all_functions, fn func ->
      has_access_permission?(caller_object, func) and
      meets_capability_requirements?(caller_object, func)
    end)
    
    accessible_functions
  end
  
  defp get_relevant_execution_history(system, goal_description) do
    # Filter execution history for relevant past executions
    system.execution_history
    |> Enum.filter(fn record ->
      goal_similarity(record, goal_description) > 0.5
    end)
    |> Enum.take(5)  # Last 5 relevant executions
  end
  
  defp parse_function_composition(reasoning_result) do
    # Parse LLM output into structured function composition
    %{
      id: generate_composition_id(),
      steps: extract_composition_steps(reasoning_result),
      dependencies: extract_dependencies(reasoning_result),
      expected_outcome: reasoning_result.action_plan,
      confidence: reasoning_result.success_probability
    }
  end
  
  defp create_execution_plan(composition) do
    # Create dependency-ordered execution plan
    dependency_graph = build_dependency_graph(composition.steps, composition.dependencies)
    execution_stages = topological_sort(dependency_graph)
    
    %{
      composition_id: composition.id,
      stages: execution_stages,
      estimated_duration: estimate_execution_duration(execution_stages)
    }
  end
  
  defp execute_parallel_stage(system, caller_object, stage) do
    # Execute all functions in a stage in parallel
    stage_results = for function_call <- stage do
      execute_llm_function_call(
        system,
        caller_object,
        function_call.function,
        function_call.intent,
        function_call.context
      )
    end
    
    %{
      stage_completed: true,
      results: stage_results,
      updated_system: system  # Simplified - would accumulate changes
    }
  end
  
  defp execute_collaborative_functions(system, objects, coordination_result) do
    # Execute functions based on coordination outcome
    execution_assignments = parse_coordination_assignments(coordination_result)
    
    collaborative_results = for {object, assignment} <- execution_assignments do
      execute_assigned_functions(system, object, assignment)
    end
    
    %{
      collaboration_completed: true,
      participating_objects: length(objects),
      individual_results: collaborative_results,
      collective_outcome: synthesize_collaborative_outcome(collaborative_results)
    }
  end
  
  # Simplified helper functions
  defp generate_function_description(object, method) do
    "Function #{method} on #{object.subtype} object"
  end
  
  defp infer_function_parameters(_object, _method) do
    [%{name: "input", type: "any", required: true}]
  end
  
  defp infer_return_type(_object, _method), do: "any"
  
  defp categorize_function(method) do
    cond do
      method in [:coordinate, :delegate] -> :coordination
      method in [:compute, :calculate] -> :computation
      method in [:send_message, :receive_message] -> :communication
      method in [:learn, :adapt] -> :learning
      true -> :computation
    end
  end
  
  defp parse_generated_parameters(_reasoning_result) do
    # Extract parameters from LLM reasoning
    %{input: "generated_input_value"}
  end
  
  defp apply_adaptation(_system, _record, adaptation) do
    %{adaptation_applied: adaptation, timestamp: DateTime.utc_now()}
  end
  
  defp has_access_permission?(caller, func) do
    # Simple permission check - in real implementation would check ACLs
    case {caller.subtype, func.access_level || :public} do
      {_, :public} -> true
      {:coordinator_object, _} -> true  # Coordinators have full access
      {_, :private} -> false
      _ -> true
    end
  end
  defp meets_capability_requirements?(_caller, _func), do: true
  defp goal_similarity(_record, _goal), do: 0.7
  defp generate_composition_id(), do: "comp_" <> (:crypto.strong_rand_bytes(4) |> Base.encode16() |> String.downcase())
  defp extract_composition_steps(_reasoning), do: [%{function: :example_function, intent: "process data"}]
  defp extract_dependencies(_reasoning), do: []
  defp build_dependency_graph(steps, _deps), do: steps
  defp topological_sort(graph), do: [graph]  # Simplified
  defp estimate_execution_duration(_stages), do: 5000  # 5 seconds
  defp calculate_success_rate(results), do: length(results) / max(1, length(results))
  defp aggregate_stage_results(_results), do: %{status: "completed"}
  defp calculate_total_execution_time(_results), do: 1500  # 1.5 seconds
  defp parse_coordination_assignments(_result), do: [{:object1, %{functions: [:task1]}}]
  defp execute_assigned_functions(_system, _object, _assignment), do: %{success: true}
  defp synthesize_collaborative_outcome(_results), do: %{collective_success: true}
  defp estimate_performance_improvement(_refinement), do: 0.15  # 15% improvement
  defp execution_time_acceptable?(_record), do: true
  defp frequent_failures?(_record), do: false
end