defmodule Dspy.EmbeddedAgent do
  @moduledoc """
  Embedded Agent Framework for DSPy
  
  This module provides a comprehensive framework for creating embedded agents that can:
  - Operate autonomously with defined goals and behaviors
  - Communicate with other agents through the AgentCommunication system
  - Learn and adapt from interactions
  - Execute tasks using various reasoning strategies
  - Maintain persistent state and memory
  """
  
  use GenServer
  require Logger
  
  alias Dspy.{
    AgentCommunication,
    ChainOfThought,
    TreeOfThoughts,
    SelfConsistency,
    EnhancedSignature
  }
  
  defstruct [
    :agent_id,
    :name,
    :role,
    :capabilities,
    :memory,
    :goals,
    :current_tasks,
    :reasoning_strategy,
    :communication_protocols,
    :learning_enabled,
    :state,
    :conversation_context,
    :performance_metrics
  ]
  
  @type agent_state :: :idle | :thinking | :executing | :communicating | :learning
  
  @type capability :: :reasoning | :planning | :learning | :vision | :coding | :analysis
  
  @type goal :: %{
    id: String.t(),
    description: String.t(),
    priority: integer(),
    status: :pending | :active | :completed,
    subtasks: [String.t()]
  }
  
  # Client API
  
  @doc """
  Start a new embedded agent
  """
  def start_link(config) do
    GenServer.start_link(__MODULE__, config, name: process_name(config.agent_id))
  end
  
  @doc """
  Create and start a new agent with the given configuration
  """
  def create_agent(config) do
    agent_id = config[:agent_id] || generate_agent_id()
    
    agent_config = %{
      agent_id: agent_id,
      name: config[:name] || "Agent_#{agent_id}",
      role: config[:role] || :general,
      capabilities: config[:capabilities] || [:reasoning, :planning],
      goals: config[:goals] || [],
      reasoning_strategy: config[:reasoning_strategy] || :chain_of_thought,
      learning_enabled: config[:learning_enabled] || true
    }
    
    case start_link(agent_config) do
      {:ok, pid} ->
        # Register with communication system
        AgentCommunication.register_agent(agent_id, agent_config)
        {:ok, pid, agent_id}
        
      error ->
        error
    end
  end
  
  @doc """
  Send a task to an agent
  """
  def assign_task(agent_id, task) do
    GenServer.call(process_name(agent_id), {:assign_task, task})
  end
  
  @doc """
  Send a message to an agent
  """
  def send_message(to_agent_id, from_agent_id, message) do
    GenServer.cast(process_name(to_agent_id), {:receive_message, from_agent_id, message})
  end
  
  @doc """
  Get the current state of an agent
  """
  def get_state(agent_id) do
    GenServer.call(process_name(agent_id), :get_state)
  end
  
  @doc """
  Request collaboration from an agent
  """
  def request_collaboration(agent_id, task, collaborators) do
    GenServer.call(process_name(agent_id), {:collaborate, task, collaborators})
  end
  
  # Server Callbacks
  
  @impl true
  def init(config) do
    state = %__MODULE__{
      agent_id: config.agent_id,
      name: config.name,
      role: config.role,
      capabilities: config.capabilities,
      memory: initialize_memory(),
      goals: config.goals || [],
      current_tasks: [],
      reasoning_strategy: config.reasoning_strategy,
      communication_protocols: [:direct, :broadcast, :collaboration],
      learning_enabled: config.learning_enabled,
      state: :idle,
      conversation_context: %{},
      performance_metrics: initialize_metrics()
    }
    
    # Schedule periodic self-reflection
    schedule_self_reflection()
    
    Logger.info("Agent #{state.name} (#{state.agent_id}) initialized")
    
    {:ok, state}
  end
  
  @impl true
  def handle_call({:assign_task, task}, _from, state) do
    Logger.info("Agent #{state.name} received task: #{inspect(task)}")
    
    # Add task to queue
    updated_tasks = [task | state.current_tasks]
    new_state = %{state | current_tasks: updated_tasks, state: :thinking}
    
    # Start processing if idle
    if state.state == :idle do
      send(self(), :process_next_task)
    end
    
    {:reply, :ok, new_state}
  end
  
  @impl true
  def handle_call(:get_state, _from, state) do
    agent_info = %{
      agent_id: state.agent_id,
      name: state.name,
      role: state.role,
      current_state: state.state,
      active_tasks: length(state.current_tasks),
      completed_goals: Enum.count(state.goals, &(&1.status == :completed)),
      memory_size: map_size(state.memory),
      performance: summarize_performance(state.performance_metrics)
    }
    
    {:reply, agent_info, state}
  end
  
  @impl true
  def handle_call({:collaborate, task, collaborators}, _from, state) do
    Logger.info("Agent #{state.name} initiating collaboration on: #{inspect(task)}")
    
    # Create collaboration request
    collaboration_id = initiate_collaboration(state, task, collaborators)
    
    {:reply, {:ok, collaboration_id}, state}
  end
  
  @impl true
  def handle_cast({:receive_message, from_agent_id, message}, state) do
    Logger.debug("Agent #{state.name} received message from #{from_agent_id}")
    
    # Process the message based on type
    new_state = process_incoming_message(state, from_agent_id, message)
    
    {:noreply, new_state}
  end
  
  @impl true
  def handle_info(:process_next_task, state) do
    case state.current_tasks do
      [] ->
        {:noreply, %{state | state: :idle}}
        
      [task | remaining_tasks] ->
        # Process the task
        new_state = process_task(state, task)
        
        # Continue with next task
        if remaining_tasks != [] do
          send(self(), :process_next_task)
        end
        
        {:noreply, %{new_state | current_tasks: remaining_tasks}}
    end
  end
  
  @impl true
  def handle_info(:self_reflect, state) do
    # Perform self-reflection and learning
    new_state = if state.learning_enabled do
      perform_self_reflection(state)
    else
      state
    end
    
    # Schedule next reflection
    schedule_self_reflection()
    
    {:noreply, new_state}
  end
  
  @impl true
  def handle_info({:new_message, message}, state) do
    # Handle new message notification from communication system
    new_state = process_communication_message(state, message)
    {:noreply, new_state}
  end
  
  # Private Functions
  
  defp process_name(agent_id) do
    String.to_atom("agent_#{agent_id}")
  end
  
  defp generate_agent_id do
    "agent_#{:crypto.strong_rand_bytes(8) |> Base.encode16()}"
  end
  
  defp initialize_memory do
    %{
      short_term: [],
      long_term: %{},
      episodic: [],
      semantic: %{}
    }
  end
  
  defp initialize_metrics do
    %{
      tasks_completed: 0,
      tasks_failed: 0,
      average_response_time: 0,
      collaboration_success_rate: 0,
      learning_improvements: []
    }
  end
  
  defp process_task(state, task) do
    start_time = System.monotonic_time(:millisecond)
    
    # Select reasoning strategy
    result = case state.reasoning_strategy do
      :chain_of_thought -> execute_with_cot(state, task)
      :tree_of_thoughts -> execute_with_tot(state, task)
      :self_consistency -> execute_with_consistency(state, task)
      _ -> execute_direct(state, task)
    end
    
    # Update metrics
    execution_time = System.monotonic_time(:millisecond) - start_time
    updated_metrics = update_performance_metrics(state.performance_metrics, result, execution_time)
    
    # Store in memory
    updated_memory = store_task_result(state.memory, task, result)
    
    %{state | 
      state: :idle,
      performance_metrics: updated_metrics,
      memory: updated_memory
    }
  end
  
  defp execute_with_cot(state, task) do
    signature = create_task_signature(task, "Chain of Thought reasoning")
    
    inputs = %{
      task: task.description,
      context: get_relevant_context(state, task),
      capabilities: state.capabilities
    }
    
    case Dspy.Module.forward(ChainOfThought.new(signature), inputs) do
      {:ok, result} -> {:success, result}
      error -> {:error, error}
    end
  end
  
  defp execute_with_tot(state, task) do
    signature = create_task_signature(task, "Tree of Thoughts exploration")
    
    inputs = %{
      task: task.description,
      context: get_relevant_context(state, task),
      capabilities: state.capabilities
    }
    
    case Dspy.Module.forward(TreeOfThoughts.new(signature), inputs) do
      {:ok, result} -> {:success, result}
      error -> {:error, error}
    end
  end
  
  defp execute_with_consistency(state, task) do
    signature = create_task_signature(task, "Self-consistency verification")
    
    inputs = %{
      task: task.description,
      context: get_relevant_context(state, task),
      capabilities: state.capabilities
    }
    
    case Dspy.Module.forward(SelfConsistency.new(signature), inputs) do
      {:ok, result} -> {:success, result}
      error -> {:error, error}
    end
  end
  
  defp execute_direct(state, task) do
    # Direct execution without special reasoning
    signature = create_task_signature(task, "Direct execution")
    
    inputs = %{
      task: task.description,
      context: get_relevant_context(state, task)
    }
    
    case Dspy.Module.forward(signature, inputs) do
      {:ok, result} -> {:success, result}
      error -> {:error, error}
    end
  end
  
  defp create_task_signature(_task, description) do
    EnhancedSignature.new("TaskExecution",
      description: description,
      input_fields: [
        %{name: :task, type: :string, required: true},
        %{name: :context, type: :map, required: false},
        %{name: :capabilities, type: :list, required: false}
      ],
      output_fields: [
        %{name: :result, type: :string, required: true},
        %{name: :reasoning, type: :string, required: true},
        %{name: :confidence, type: :float, required: true}
      ]
    )
  end
  
  defp get_relevant_context(state, task) do
    # Retrieve relevant context from memory
    %{
      recent_tasks: Enum.take(state.memory.short_term, 5),
      related_knowledge: Map.get(state.memory.semantic, task.category, %{}),
      current_goals: Enum.filter(state.goals, &(&1.status == :active))
    }
  end
  
  defp process_incoming_message(state, from_agent_id, message) do
    case message.type do
      :query ->
        handle_query(state, from_agent_id, message)
        
      :collaboration_request ->
        handle_collaboration_request(state, from_agent_id, message)
        
      :knowledge_share ->
        handle_knowledge_share(state, from_agent_id, message)
        
      _ ->
        # Store in conversation context
        update_conversation_context(state, from_agent_id, message)
    end
  end
  
  defp handle_query(state, from_agent_id, query) do
    # Process query and send response
    response = process_query(state, query)
    
    AgentCommunication.send_message(
      state.agent_id,
      from_agent_id,
      response,
      thread_id: query.thread_id
    )
    
    state
  end
  
  defp handle_collaboration_request(state, from_agent_id, request) do
    # Evaluate if we can help with the task
    if can_help_with_task?(state, request.task) do
      # Accept collaboration
      AgentCommunication.send_message(
        state.agent_id,
        from_agent_id,
        %{type: :collaboration_accepted, task_id: request.task_id},
        thread_id: request.thread_id
      )
      
      # Add as current task
      %{state | current_tasks: [request.task | state.current_tasks]}
    else
      # Decline collaboration
      AgentCommunication.send_message(
        state.agent_id,
        from_agent_id,
        %{type: :collaboration_declined, reason: :insufficient_capabilities},
        thread_id: request.thread_id
      )
      
      state
    end
  end
  
  defp handle_knowledge_share(state, _from_agent_id, knowledge) do
    # Integrate shared knowledge into memory
    updated_memory = integrate_knowledge(state.memory, knowledge)
    %{state | memory: updated_memory}
  end
  
  defp update_conversation_context(state, agent_id, message) do
    updated_context = Map.update(state.conversation_context, agent_id, [message], fn history ->
      [message | history] |> Enum.take(10) # Keep last 10 messages
    end)
    
    %{state | conversation_context: updated_context}
  end
  
  defp initiate_collaboration(state, task, collaborators) do
    # Request collaboration through communication system
    {:ok, collaboration_id} = AgentCommunication.request_collaboration(
      state.agent_id,
      collaborators,
      task,
      required_capabilities: task.required_capabilities
    )
    
    collaboration_id
  end
  
  defp perform_self_reflection(state) do
    # Analyze recent performance
    recent_performance = analyze_recent_performance(state)
    
    # Identify areas for improvement
    improvements = identify_improvements(state, recent_performance)
    
    # Update learning if improvements found
    if improvements != [] do
      updated_memory = apply_learning(state.memory, improvements)
      updated_metrics = record_learning_improvement(state.performance_metrics, improvements)
      
      %{state | memory: updated_memory, performance_metrics: updated_metrics}
    else
      state
    end
  end
  
  defp process_communication_message(state, message) do
    case message.protocol do
      :direct ->
        process_incoming_message(state, message.from, message.content)
        
      :broadcast ->
        # Handle broadcast message
        if relevant_to_agent?(state, message.content) do
          process_incoming_message(state, message.from, message.content)
        else
          state
        end
        
      :negotiation ->
        # Handle negotiation protocol
        handle_negotiation(state, message)
        
      _ ->
        state
    end
  end
  
  defp can_help_with_task?(state, task) do
    required_capabilities = task.required_capabilities || []
    Enum.all?(required_capabilities, &(&1 in state.capabilities))
  end
  
  defp relevant_to_agent?(state, content) do
    # Check if broadcast is relevant to this agent
    content.topics == :all or
    Enum.any?(content.topics, &(&1 in state.capabilities))
  end
  
  defp handle_negotiation(state, _message) do
    # Handle negotiation messages
    state
  end
  
  defp store_task_result(memory, task, result) do
    # Store in short-term memory
    updated_short_term = [{task, result, DateTime.utc_now()} | memory.short_term]
    |> Enum.take(100) # Keep last 100 entries
    
    # Update semantic memory if successful
    updated_semantic = if elem(result, 0) == :success do
      Map.update(memory.semantic, task.category || :general, [result], fn existing ->
        [result | existing] |> Enum.take(50)
      end)
    else
      memory.semantic
    end
    
    %{memory | short_term: updated_short_term, semantic: updated_semantic}
  end
  
  defp update_performance_metrics(metrics, result, execution_time) do
    case result do
      {:success, _} ->
        %{metrics |
          tasks_completed: metrics.tasks_completed + 1,
          average_response_time: update_average(
            metrics.average_response_time,
            execution_time,
            metrics.tasks_completed + 1
          )
        }
        
      {:error, _} ->
        %{metrics | tasks_failed: metrics.tasks_failed + 1}
    end
  end
  
  defp update_average(current_avg, new_value, count) do
    ((current_avg * (count - 1)) + new_value) / count
  end
  
  defp analyze_recent_performance(state) do
    # Analyze recent task results
    recent_tasks = Enum.take(state.memory.short_term, 20)
    
    %{
      success_rate: calculate_success_rate(recent_tasks),
      average_time: calculate_average_time(recent_tasks),
      error_patterns: identify_error_patterns(recent_tasks)
    }
  end
  
  defp identify_improvements(_state, performance) do
    improvements = []
    
    if performance.success_rate < 0.7 do
      [:improve_reasoning_accuracy | improvements]
    else
      improvements
    end
  end
  
  defp apply_learning(memory, improvements) do
    # Apply learned improvements to memory
    Enum.reduce(improvements, memory, fn improvement, acc ->
      case improvement do
        :improve_reasoning_accuracy ->
          Map.put(acc, :learned_patterns, identify_successful_patterns(acc))
          
        _ ->
          acc
      end
    end)
  end
  
  defp record_learning_improvement(metrics, improvements) do
    %{metrics |
      learning_improvements: improvements ++ metrics.learning_improvements
    }
  end
  
  defp integrate_knowledge(memory, shared_knowledge) do
    # Integrate shared knowledge into semantic memory
    updated_semantic = Map.merge(memory.semantic, shared_knowledge.semantic || %{})
    %{memory | semantic: updated_semantic}
  end
  
  defp process_query(_state, query) do
    %{
      type: :query_response,
      query_id: query.id,
      answer: "Processing query: #{query.content}",
      confidence: 0.85
    }
  end
  
  defp summarize_performance(metrics) do
    %{
      total_tasks: metrics.tasks_completed + metrics.tasks_failed,
      success_rate: if metrics.tasks_completed + metrics.tasks_failed > 0 do
        metrics.tasks_completed / (metrics.tasks_completed + metrics.tasks_failed)
      else
        0.0
      end,
      avg_response_time_ms: metrics.average_response_time
    }
  end
  
  defp calculate_success_rate(tasks) do
    if tasks == [] do
      1.0
    else
      successful = Enum.count(tasks, fn {_, result, _} -> elem(result, 0) == :success end)
      successful / length(tasks)
    end
  end
  
  defp calculate_average_time(_tasks) do
    # Placeholder - would calculate from actual timing data
    150.0
  end
  
  defp identify_error_patterns(tasks) do
    # Identify common error patterns
    tasks
    |> Enum.filter(fn {_, result, _} -> elem(result, 0) == :error end)
    |> Enum.map(fn {_, {:error, reason}, _} -> reason end)
    |> Enum.frequencies()
  end
  
  defp identify_successful_patterns(_memory) do
    # Extract patterns from successful operations
    %{strategies: [:adaptive_reasoning, :context_awareness]}
  end
  
  defp schedule_self_reflection do
    Process.send_after(self(), :self_reflect, 60_000) # Every minute
  end
end