lux

defmodule Lux.Reflection do
  @moduledoc """
  A Reflection represents a Agent's decision-making process and self-awareness.
  It can evolve over time as the Agent learns and adapts to new situations.
  """

  @type t :: %__MODULE__{
          id: String.t(),
          name: String.t(),
          description: String.t(),
          context: map(),
          llm_config: map(),
          history: [map()],
          last_reflection_time: DateTime.t() | nil,
          # Learned patterns and behaviors
          patterns: [map()],
          # Performance metrics
          metrics: map(),
          state: :idle | :reflecting | :learning | :adapting
        }

  defstruct id: nil,
            name: "",
            description: "",
            context: %{},
            llm_config: %{
              provider: :openai,
              model: "gpt-4",
              temperature: 0.7,
              max_tokens: 1000
            },
            history: [],
            last_reflection_time: nil,
            patterns: [],
            metrics: %{
              successful_actions: 0,
              failed_actions: 0,
              avg_response_time: 0,
              learning_rate: 0.1,
              total_reflections: 0,
              total_actions: 0
            },
            state: :idle

  @doc """
  Creates a new reflection module.
  """
  def new(attrs \\ %{}) do
    struct(
      __MODULE__,
      Map.merge(attrs, %{
        id: attrs[:id] || Lux.UUID.generate(),
        last_reflection_time: DateTime.utc_now()
      })
    )
  end

  @doc """
  Performs a reflection cycle for a agent, deciding on next actions.
  """
  def reflect(%__MODULE__{} = reflection, %Lux.Agent{} = agent, context) when is_map(context) do
    reflection = %{reflection | state: :reflecting}

    prompt = build_reflection_prompt(reflection, agent, context)

    case call_llm(prompt, reflection.llm_config) do
      {:ok, response} ->
        try do
          {actions, new_patterns} = parse_reflection_response(response)

          updated_reflection =
            reflection
            |> update_history(response)
            |> update_patterns(new_patterns)
            |> update_metrics(actions)
            |> Map.put(:last_reflection_time, DateTime.utc_now())
            |> Map.put(:state, :idle)

          {:ok, actions, updated_reflection}
        rescue
          error -> {:error, error, %{reflection | state: :idle}}
        end
    end
  end

  def reflect(%__MODULE__{} = reflection, _agent, _context) do
    {:error, :invalid_context, %{reflection | state: :idle}}
  end

  @doc """
  Updates the reflection's context with new information.
  """
  def update_context(%__MODULE__{} = reflection, new_context) do
    merged_context = Map.merge(reflection.context, new_context)
    %{reflection | context: merged_context}
  end

  @doc """
  Analyzes the reflection's history to identify patterns and improve decision making.
  """
  def learn(%__MODULE__{} = reflection) do
    reflection = %{reflection | state: :learning}

    # Analyze history and update patterns
    new_patterns = analyze_history(reflection.history)

    # Update metrics based on learning
    new_metrics = update_learning_metrics(reflection.metrics, new_patterns)

    %{reflection | patterns: new_patterns, metrics: new_metrics, state: :idle}
  end

  # Private helpers

  defp build_reflection_prompt(reflection, agent, context) do
    """
    You are #{agent.name}'s reflection process.
    Your goal is to help achieve: #{agent.goal}

    Current context:
    #{inspect(context)}

    Recent history:
    #{format_history(reflection.history)}

    Learned patterns:
    #{format_patterns(reflection.patterns)}

    Performance metrics:
    #{inspect(reflection.metrics)}

    Based on this information, what actions should the agent take?
    Respond in the following JSON format:
    {
      "reflection": {
        "thoughts": "your reasoning process",
        "patterns_identified": [],
        "improvement_suggestions": []
      },
      "actions": [
        {
          "type": "prism|beam|lens",
          "name": "action_name",
          "params": {},
          "expected_outcome": "description"
        }
      ]
    }
    """
  end

  # This is here so that the compiler doesn't complain about the SamplePrism
  # not being an existing atom. It will be replaced once we implemt call_llm
  SamplePrism

  defp call_llm(_prompt, _config) do
    # TODO: Implement actual LLM call
    {:ok,
     %{
       "reflection" => %{
         "thoughts" => "Based on past patterns and current context...",
         "patterns_identified" => ["Pattern1", "Pattern2"],
         "improvement_suggestions" => ["Suggestion1"]
       },
       "actions" => [
         %{
           "type" => "prism",
           "name" => "SamplePrism",
           "params" => %{},
           "expected_outcome" => "Expected result"
         }
       ]
     }}
  end

  defp parse_reflection_response(response) do
    actions =
      Enum.map(response["actions"], fn action ->
        {String.to_existing_atom("Elixir.#{action["name"]}"), action["params"]}
      end)

    patterns = response["reflection"]["patterns_identified"]

    {actions, patterns}
  end

  defp update_history(reflection, response) do
    new_history_entry = %{
      timestamp: DateTime.utc_now(),
      reflection: response["reflection"],
      actions: response["actions"]
    }

    # Keep last 100 reflections
    updated_history = Enum.take([new_history_entry | reflection.history], 100)
    %{reflection | history: updated_history}
  end

  # Make testable
  @doc false
  def format_history(history) do
    history
    # Show only last 5 reflections
    |> Enum.take(5)
    |> Enum.map_join("\n", fn entry ->
      "#{entry.timestamp}: #{inspect(entry.reflection["thoughts"])}"
    end)
  end

  defp format_patterns(patterns) do
    Enum.map_join(patterns, "\n", &inspect/1)
  end

  # Make testable
  @doc false
  def update_metrics(reflection, actions) do
    # Update metrics based on new actions
    new_metrics = %{
      reflection.metrics
      | total_reflections: (reflection.metrics[:total_reflections] || 0) + 1,
        total_actions: (reflection.metrics[:total_actions] || 0) + length(actions)
    }

    %{reflection | metrics: new_metrics}
  end

  # Make testable
  @doc false
  def update_patterns(reflection, new_patterns) do
    # Merge new patterns with existing ones, removing duplicates
    updated_patterns =
      (reflection.patterns ++ new_patterns)
      |> Enum.uniq()
      # Keep only top 50 patterns
      |> Enum.take(50)

    %{reflection | patterns: updated_patterns}
  end

  defp analyze_history(_history) do
    # TODO: Implement pattern recognition from history
    []
  end

  defp update_learning_metrics(metrics, _new_patterns) do
    # Update metrics based on learning process
    Map.merge(metrics, %{
      # Decrease learning rate over time
      learning_rate: metrics.learning_rate * 0.9,
      total_reflections: metrics.total_reflections + 1
    })
  end
end