defmodule Object.AIReasoning do
  @moduledoc """
  Advanced AI reasoning capabilities for AAOS objects using DSPy framework.
  Provides pre-built signatures for common object behaviors and interactions.
  """

  alias Object.DSPyBridge

  @common_signatures %{
    message_analysis: %{
      description: "Analyze incoming messages for intent, priority, and required actions",
      inputs: [
        sender: "ID of the message sender",
        content: "Message content to analyze", 
        context: "Current object state and recent interactions"
      ],
      outputs: [
        intent: "Identified intent or purpose of the message",
        priority: "Priority level (high/medium/low)",
        suggested_actions: "List of recommended response actions",
        confidence: "Confidence score for the analysis"
      ],
      instructions: "Analyze the message considering the object's current state and interaction history. Provide clear intent classification and actionable recommendations."
    },

    behavior_adaptation: %{
      description: "Adapt object behavior based on performance feedback and environmental changes",
      inputs: [
        current_behavior: "Description of current behavior patterns",
        performance_metrics: "Recent performance data and feedback",
        environment_state: "Current environmental conditions",
        goals: "Object's current goals and objectives"
      ],
      outputs: [
        behavior_adjustments: "Specific behavior modifications to implement",
        reasoning: "Explanation of why these adjustments are beneficial",
        expected_outcomes: "Predicted results of the behavior changes",
        risk_assessment: "Potential risks and mitigation strategies"
      ],
      instructions: "Evaluate current performance and suggest evidence-based behavior adaptations that align with the object's goals while minimizing risks."
    },

    interaction_planning: %{
      description: "Plan optimal interaction strategies with other objects or agents",
      inputs: [
        target_objects: "List of objects/agents to interact with",
        interaction_goal: "Desired outcome of the interaction",
        available_resources: "Resources available for the interaction",
        constraints: "Any limitations or constraints to consider"
      ],
      outputs: [
        interaction_plan: "Step-by-step interaction strategy",
        communication_approach: "Recommended communication style and content",
        timing: "Optimal timing for the interaction",
        fallback_strategies: "Alternative approaches if primary plan fails"
      ],
      instructions: "Design an effective interaction plan that maximizes the likelihood of achieving the goal while respecting constraints and maintaining good relationships."
    },

    problem_solving: %{
      description: "Systematic problem-solving using chain-of-thought reasoning",
      inputs: [
        problem_description: "Clear description of the problem to solve",
        available_information: "All relevant information and data",
        constraints: "Limitations and requirements to consider",
        success_criteria: "How to measure successful resolution"
      ],
      outputs: [
        problem_analysis: "Breakdown of the problem into components",
        solution_approach: "Step-by-step solution methodology",
        implementation_plan: "Concrete steps to implement the solution",
        verification_method: "How to verify the solution works"
      ],
      instructions: "Use systematic reasoning to analyze the problem, develop a comprehensive solution, and create a clear implementation plan with verification steps."
    },

    learning_synthesis: %{
      description: "Synthesize learning from experiences and update knowledge base",
      inputs: [
        experiences: "Recent experiences and outcomes",
        existing_knowledge: "Current knowledge and beliefs",
        feedback: "External feedback received",
        context: "Environmental and situational context"
      ],
      outputs: [
        key_insights: "Important insights extracted from experiences",
        knowledge_updates: "Updates to make to knowledge base",
        pattern_recognition: "Identified patterns and relationships",
        future_applications: "How to apply learnings in future situations"
      ],
      instructions: "Extract meaningful insights from experiences, identify patterns, and determine how to update knowledge for improved future performance."
    }
  }

  @doc """
  Initializes AI reasoning capabilities for an object by starting a DSPy bridge
  and registering common reasoning signatures.
  
  ## Parameters
  - `object_id`: The ID of the object to initialize reasoning for
  
  ## Returns
  - `{:ok, object_id}` on successful initialization
  - `{:error, reason}` if initialization fails
  
  ## Examples
      iex> Object.AIReasoning.initialize_object_reasoning("agent_1")
      {:ok, "agent_1"}
  """
  def initialize_object_reasoning(object_id) do
    case DSPyBridge.start_link(object_id) do
      {:ok, _pid} ->
        register_common_signatures(object_id)
        {:ok, object_id}
      
      {:error, reason} ->
        {:error, "Failed to initialize reasoning: #{inspect(reason)}"}
    end
  end

  @doc """
  Analyzes incoming messages using AI reasoning to determine intent, priority, and recommended actions.
  
  ## Parameters
  - `object_id`: The ID of the reasoning object
  - `sender`: ID of the message sender
  - `content`: Message content to analyze
  - `context`: Current object state and interaction history
  
  ## Returns
  AI analysis result containing intent, priority, suggested actions, and confidence score
  """
  def analyze_message(object_id, sender, content, context) do
    DSPyBridge.reason_with_signature(object_id, :message_analysis, %{
      sender: sender,
      content: content,
      context: context
    })
  end

  @doc """
  Adapts object behavior based on performance feedback and environmental changes.
  
  ## Parameters
  - `object_id`: The ID of the reasoning object
  - `current_behavior`: Description of current behavior patterns
  - `metrics`: Recent performance data and feedback
  - `environment`: Current environmental conditions
  - `goals`: Object's current goals and objectives
  
  ## Returns
  Behavior adaptation recommendations with reasoning and risk assessment
  """
  def adapt_behavior(object_id, current_behavior, metrics, environment, goals) do
    DSPyBridge.reason_with_signature(object_id, :behavior_adaptation, %{
      current_behavior: current_behavior,
      performance_metrics: metrics,
      environment_state: environment,
      goals: goals
    })
  end

  @doc """
  Plans optimal interaction strategies with other objects or agents.
  
  ## Parameters
  - `object_id`: The ID of the reasoning object
  - `targets`: List of objects/agents to interact with
  - `goal`: Desired outcome of the interaction
  - `resources`: Resources available for the interaction
  - `constraints`: Any limitations or constraints to consider
  
  ## Returns
  Interaction plan with strategy, timing, and fallback options
  """
  def plan_interaction(object_id, targets, goal, resources, constraints) do
    DSPyBridge.reason_with_signature(object_id, :interaction_planning, %{
      target_objects: targets,
      interaction_goal: goal,
      available_resources: resources,
      constraints: constraints
    })
  end

  @doc """
  Performs systematic problem-solving using chain-of-thought reasoning.
  
  ## Parameters
  - `object_id`: The ID of the reasoning object
  - `problem`: Clear description of the problem to solve
  - `information`: All relevant information and data
  - `constraints`: Limitations and requirements to consider
  - `criteria`: How to measure successful resolution
  
  ## Returns
  Problem analysis, solution approach, implementation plan, and verification method
  """
  def solve_problem(object_id, problem, information, constraints, criteria) do
    DSPyBridge.reason_with_signature(object_id, :problem_solving, %{
      problem_description: problem,
      available_information: information,
      constraints: constraints,
      success_criteria: criteria
    })
  end

  @doc """
  Synthesizes learning from experiences and updates knowledge base.
  
  ## Parameters
  - `object_id`: The ID of the reasoning object
  - `experiences`: Recent experiences and outcomes
  - `knowledge`: Current knowledge and beliefs
  - `feedback`: External feedback received
  - `context`: Environmental and situational context
  
  ## Returns
  Key insights, knowledge updates, pattern recognition, and future applications
  """
  def synthesize_learning(object_id, experiences, knowledge, feedback, context) do
    DSPyBridge.reason_with_signature(object_id, :learning_synthesis, %{
      experiences: experiences,
      existing_knowledge: knowledge,
      feedback: feedback,
      context: context
    })
  end

  @doc """
  Registers a custom DSPy signature for specific reasoning tasks.
  
  ## Parameters
  - `object_id`: The ID of the reasoning object
  - `name`: Name for the custom signature
  - `signature_spec`: Specification of inputs, outputs, and instructions
  
  ## Returns
  `:ok` on successful registration
  """
  def register_custom_signature(object_id, name, signature_spec) do
    DSPyBridge.register_signature(object_id, name, signature_spec)
  end

  @doc """
  Gets performance metrics for the reasoning system.
  
  ## Parameters
  - `object_id`: The ID of the reasoning object
  
  ## Returns
  Performance metrics including query count, cache hits, and average latency
  """
  def get_reasoning_performance(object_id) do
    DSPyBridge.get_reasoning_metrics(object_id)
  end

  defp register_common_signatures(object_id) do
    Enum.each(@common_signatures, fn {name, spec} ->
      DSPyBridge.register_signature(object_id, name, spec)
    end)
  end
end