defmodule Automata.CollectiveIntelligence.KnowledgeSynthesis.KnowledgeSystem do
  @moduledoc """
  Integrates the multi-level knowledge synthesis components into a cohesive system.
  
  This module serves as the main entry point for the Multi-Level Knowledge Synthesis
  framework, providing a unified API for knowledge creation, storage, retrieval,
  synthesis, and analysis across all knowledge levels.
  """
  
  alias Automata.CollectiveIntelligence.KnowledgeSynthesis.KnowledgeRepresentation
  alias Automata.CollectiveIntelligence.KnowledgeSynthesis.KnowledgeSynthesis
  alias Automata.CollectiveIntelligence.KnowledgeSynthesis.MultilevelKnowledgeStore
  
  alias KnowledgeRepresentation.KnowledgeAtom
  alias KnowledgeRepresentation.KnowledgeTriple
  alias KnowledgeRepresentation.KnowledgeFrame
  alias KnowledgeRepresentation.KnowledgeGraph
  alias KnowledgeRepresentation.HierarchicalConcept
  
  # System initialization
  
  @doc """
  Starts the knowledge system.
  """
  def start_link(opts \\ []) do
    # Start the multilevel knowledge store
    store_name = Keyword.get(opts, :store_name, MultilevelKnowledgeStore)
    
    {:ok, store_pid} = MultilevelKnowledgeStore.start_link(
      name: store_name,
      auto_synthesis: Keyword.get(opts, :auto_synthesis, true),
      auto_consistency: Keyword.get(opts, :auto_consistency, true),
      synthesis_threshold: Keyword.get(opts, :synthesis_threshold, 10)
    )
    
    {:ok, store_pid}
  end
  
  # Knowledge creation functions
  
  @doc """
  Creates a new knowledge atom.
  """
  def create_atom(content, source, opts \\ []) do
    # Create atom
    atom = KnowledgeAtom.new(content, source, opts)
    
    # Add to store
    MultilevelKnowledgeStore.add_atom(atom)
  end
  
  @doc """
  Creates a new knowledge triple.
  """
  def create_triple(subject, predicate, object, source, opts \\ []) do
    # Create triple
    triple = KnowledgeTriple.new(subject, predicate, object, source, opts)
    
    # Add to store
    MultilevelKnowledgeStore.add_triple(triple)
  end
  
  @doc """
  Creates a new knowledge frame.
  """
  def create_frame(name, slots \\ %{}, opts \\ []) do
    # Create frame
    frame = KnowledgeFrame.new(name, slots, opts)
    
    # Add to store
    MultilevelKnowledgeStore.add_frame(frame)
  end
  
  @doc """
  Creates a new knowledge graph.
  """
  def create_graph(name, opts \\ []) do
    # Create empty graph
    graph = KnowledgeGraph.new(name, opts)
    
    # Add to store
    MultilevelKnowledgeStore.add_graph(graph)
  end
  
  @doc """
  Creates a new hierarchical concept.
  """
  def create_concept(name, description, opts \\ []) do
    # Create concept
    concept = HierarchicalConcept.new(name, description, opts)
    
    # Add to store
    MultilevelKnowledgeStore.add_concept(concept)
  end
  
  # Knowledge retrieval functions
  
  @doc """
  Retrieves a knowledge atom by ID.
  """
  def get_atom(atom_id) do
    MultilevelKnowledgeStore.get_atom(atom_id)
  end
  
  @doc """
  Retrieves a knowledge triple by ID.
  """
  def get_triple(triple_id) do
    MultilevelKnowledgeStore.get_triple(triple_id)
  end
  
  @doc """
  Retrieves a knowledge frame by ID.
  """
  def get_frame(frame_id) do
    MultilevelKnowledgeStore.get_frame(frame_id)
  end
  
  @doc """
  Retrieves a knowledge graph by ID.
  """
  def get_graph(graph_id) do
    MultilevelKnowledgeStore.get_graph(graph_id)
  end
  
  @doc """
  Retrieves a hierarchical concept by ID.
  """
  def get_concept(concept_id) do
    MultilevelKnowledgeStore.get_concept(concept_id)
  end
  
  # Knowledge query functions
  
  @doc """
  Queries knowledge atoms based on criteria.
  """
  def query_atoms(criteria) do
    MultilevelKnowledgeStore.query_atoms(criteria)
  end
  
  @doc """
  Queries knowledge triples based on criteria.
  """
  def query_triples(criteria) do
    MultilevelKnowledgeStore.query_triples(criteria)
  end
  
  @doc """
  Performs a graph pattern match query.
  """
  def graph_pattern_match(pattern) do
    MultilevelKnowledgeStore.graph_pattern_match(pattern)
  end
  
  @doc """
  Traverses concept hierarchy from a starting concept.
  """
  def traverse_concept_hierarchy(starting_concept_id, direction \\ :down, max_depth \\ 3) do
    MultilevelKnowledgeStore.traverse_concept_hierarchy(starting_concept_id, direction, max_depth)
  end
  
  # Knowledge synthesis functions
  
  @doc """
  Synthesizes higher-level knowledge based on current knowledge store content.
  """
  def synthesize(level \\ :all, options \\ []) do
    MultilevelKnowledgeStore.synthesize(level, options)
  end
  
  @doc """
  Verifies the consistency of the knowledge store.
  """
  def verify_consistency(level \\ :all) do
    MultilevelKnowledgeStore.verify_consistency(level)
  end
  
  # Knowledge graph manipulation functions
  
  @doc """
  Adds an entity to a knowledge graph.
  """
  def add_entity_to_graph(graph_id, type, properties, opts \\ []) do
    # Get the graph
    case MultilevelKnowledgeStore.get_graph(graph_id) do
      {:ok, graph} ->
        # Add entity
        updated_graph = KnowledgeGraph.add_entity(graph, type, properties, opts)
        
        # Add updated graph to store
        MultilevelKnowledgeStore.add_graph(updated_graph)
        
      {:error, reason} ->
        {:error, reason}
    end
  end
  
  @doc """
  Adds a relationship to a knowledge graph.
  """
  def add_relationship_to_graph(graph_id, type, from_id, to_id, properties \\ %{}, opts \\ []) do
    # Get the graph
    case MultilevelKnowledgeStore.get_graph(graph_id) do
      {:ok, graph} ->
        # Add relationship
        updated_graph = KnowledgeGraph.add_relationship(graph, type, from_id, to_id, properties, opts)
        
        # Add updated graph to store
        MultilevelKnowledgeStore.add_graph(updated_graph)
        
      {:error, reason} ->
        {:error, reason}
    end
  end
  
  @doc """
  Finds paths between two entities in a knowledge graph.
  """
  def find_entity_paths(graph_id, from_id, to_id, max_depth \\ 3) do
    # Get the graph
    case MultilevelKnowledgeStore.get_graph(graph_id) do
      {:ok, graph} ->
        # Find paths
        KnowledgeGraph.find_paths(graph, from_id, to_id, max_depth)
        
      {:error, reason} ->
        {:error, reason}
    end
  end
  
  # Frame manipulation functions
  
  @doc """
  Sets a slot value in a knowledge frame.
  """
  def set_frame_slot(frame_id, slot_name, value, confidence \\ 1.0) do
    # Get the frame
    case MultilevelKnowledgeStore.get_frame(frame_id) do
      {:ok, frame} ->
        # Set slot
        updated_frame = KnowledgeFrame.set_slot(frame, slot_name, value, confidence)
        
        # Add updated frame to store
        MultilevelKnowledgeStore.add_frame(updated_frame)
        
      {:error, reason} ->
        {:error, reason}
    end
  end
  
  @doc """
  Gets a slot value from a knowledge frame.
  """
  def get_frame_slot(frame_id, slot_name) do
    # Get the frame
    case MultilevelKnowledgeStore.get_frame(frame_id) do
      {:ok, frame} ->
        # Get slot value
        value = KnowledgeFrame.get_slot(frame, slot_name)
        {:ok, value}
        
      {:error, reason} ->
        {:error, reason}
    end
  end
  
  @doc """
  Merges two knowledge frames.
  """
  def merge_frames(frame_id1, frame_id2) do
    # Get the frames
    with {:ok, frame1} <- MultilevelKnowledgeStore.get_frame(frame_id1),
         {:ok, frame2} <- MultilevelKnowledgeStore.get_frame(frame_id2) do
      # Merge frames
      merged_frame = KnowledgeFrame.merge(frame1, frame2)
      
      # Add merged frame to store
      MultilevelKnowledgeStore.add_frame(merged_frame)
    else
      {:error, reason} -> {:error, reason}
    end
  end
  
  # Concept manipulation functions
  
  @doc """
  Adds a child concept to a parent concept.
  """
  def add_child_concept(parent_id, child_id) do
    # Get both concepts
    with {:ok, parent} <- MultilevelKnowledgeStore.get_concept(parent_id),
         {:ok, child} <- MultilevelKnowledgeStore.get_concept(child_id) do
      # Add child to parent
      {updated_parent, updated_child} = HierarchicalConcept.add_child(parent, child)
      
      # Add updated concepts to store
      {:ok, _} = MultilevelKnowledgeStore.add_concept(updated_parent)
      {:ok, _} = MultilevelKnowledgeStore.add_concept(updated_child)
      
      {:ok, {updated_parent.id, updated_child.id}}
    else
      {:error, reason} -> {:error, reason}
    end
  end
  
  @doc """
  Adds an attribute to a concept.
  """
  def add_concept_attribute(concept_id, key, value, confidence \\ 1.0) do
    # Get the concept
    case MultilevelKnowledgeStore.get_concept(concept_id) do
      {:ok, concept} ->
        # Add attribute
        updated_concept = HierarchicalConcept.add_attribute(concept, key, value, confidence)
        
        # Add updated concept to store
        MultilevelKnowledgeStore.add_concept(updated_concept)
        
      {:error, reason} ->
        {:error, reason}
    end
  end
  
  @doc """
  Adds a relation between concepts.
  """
  def add_concept_relation(concept_id, relation_type, target_id, metadata \\ %{}) do
    # Get the concept
    case MultilevelKnowledgeStore.get_concept(concept_id) do
      {:ok, concept} ->
        # Add relation
        updated_concept = HierarchicalConcept.add_relation(concept, relation_type, target_id, metadata)
        
        # Add updated concept to store
        MultilevelKnowledgeStore.add_concept(updated_concept)
        
      {:error, reason} ->
        {:error, reason}
    end
  end
  
  # Advanced knowledge extraction functions
  
  @doc """
  Extracts a conceptual model from the knowledge store.
  """
  def extract_conceptual_model(domain_name, options \\ []) do
    # This is a complex operation that analyzes the knowledge store
    # to extract a coherent conceptual model for a specific domain
    
    # Get relevant concepts
    {:ok, domain_concepts} = MultilevelKnowledgeStore.query_atoms([
      content_regex: ~r/#{domain_name}/i
    ])
    
    # Get related triples
    {:ok, domain_triples} = MultilevelKnowledgeStore.query_triples([
      predicate: :relates_to,
      object: domain_name
    ])
    
    # Extract key concepts and relationships
    # This would involve analyzing the concepts and triples
    # to identify important domain elements
    
    # For now, return a simple representation
    model = %{
      domain: domain_name,
      key_concepts: Enum.map(domain_concepts, & &1.content),
      key_relationships: Enum.map(domain_triples, fn triple ->
        {triple.subject, triple.predicate, triple.object}
      end)
    }
    
    {:ok, model}
  end
  
  @doc """
  Identifies contradictions in the knowledge store.
  """
  def identify_contradictions do
    # Verify consistency to find contradictions
    case MultilevelKnowledgeStore.verify_consistency(:all) do
      {:ok, _} ->
        # No contradictions found
        {:ok, []}
        
      {:inconsistent, results} ->
        # Extract contradictions from results
        contradictions = extract_contradictions(results)
        {:ok, contradictions}
    end
  end
  
  @doc """
  Analyzes knowledge gaps in the store.
  """
  def analyze_knowledge_gaps(domain_concepts) do
    # This function would analyze the knowledge store to identify
    # areas where knowledge is sparse or missing
    
    # Get all concepts
    concepts = Enum.map(domain_concepts, fn concept ->
      case MultilevelKnowledgeStore.get_concept(concept) do
        {:ok, concept_data} -> concept_data
        _ -> nil
      end
    end)
    |> Enum.reject(&is_nil/1)
    
    # Identify gaps in relationships
    gaps = Enum.map(concepts, fn concept ->
      missing_relations = identify_missing_relations(concept, concepts)
      
      if Enum.empty?(missing_relations) do
        nil
      else
        {concept.id, missing_relations}
      end
    end)
    |> Enum.reject(&is_nil/1)
    |> Enum.into(%{})
    
    {:ok, gaps}
  end
  
  # Private functions
  
  defp extract_contradictions(results) do
    # Extract atom contradictions
    atom_contradictions = case results[:atom] do
      {:inconsistent, issues} -> issues
      _ -> []
    end
    
    # Extract triple contradictions
    triple_contradictions = case results[:triple] do
      {:inconsistent, issues} -> issues
      _ -> []
    end
    
    # Extract graph contradictions
    graph_contradictions = case results[:graph] do
      {:inconsistent, issues} -> issues
      _ -> []
    end
    
    # Combine all contradictions
    %{
      atoms: atom_contradictions,
      triples: triple_contradictions,
      graphs: graph_contradictions
    }
  end
  
  defp identify_missing_relations(concept, all_concepts) do
    # Get all possible relation types from other concepts
    all_relation_types = Enum.reduce(all_concepts, MapSet.new(), fn c, acc ->
      relation_types = Map.keys(c.relations) |> MapSet.new()
      MapSet.union(acc, relation_types)
    end)
    
    # Get this concept's relation types
    concept_relation_types = Map.keys(concept.relations) |> MapSet.new()
    
    # Find missing relation types
    missing_types = MapSet.difference(all_relation_types, concept_relation_types)
    
    # Estimate importance of each missing relation
    Enum.map(missing_types, fn type ->
      # Count how many concepts have this relation type
      count = Enum.count(all_concepts, fn c ->
        Map.has_key?(c.relations, type)
      end)
      
      # Calculate importance based on frequency
      importance = count / length(all_concepts)
      
      {type, importance}
    end)
    |> Enum.sort_by(fn {_type, importance} -> importance end, :desc)
  end
end