Class: Llmemory::LongTerm::GraphBased::Memory

Inherits:
Object
  • Object
show all
Includes:
MemoryModule
Defined in:
lib/llmemory/long_term/graph_based/memory.rb

Instance Attribute Summary collapse

Instance Method Summary collapse

Methods included from MemoryModule

#forget_log, #read

Constructor Details

#initialize(user_id:, storage: nil, vector_store: nil, llm: nil, extractor: nil) ⇒ Memory

Returns a new instance of Memory.



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# File 'lib/llmemory/long_term/graph_based/memory.rb', line 17

def initialize(user_id:, storage: nil, vector_store: nil, llm: nil, extractor: nil)
  @user_id = user_id
  @graph_storage = storage || Storages.build
  @kg = KnowledgeGraph.new(user_id: user_id, storage: @graph_storage)
  @conflict_resolver = ConflictResolver.new(@kg)
  @vector_store = vector_store || build_vector_store
  @llm = llm || Llmemory::LLM.client
  @extractor = extractor || Llmemory::Extractors::EntityRelationExtractor.new(llm: @llm)
end

Instance Attribute Details

#user_id ⇒ Object (readonly)

Returns the value of attribute user_id.



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# File 'lib/llmemory/long_term/graph_based/memory.rb', line 103

def user_id
  @user_id
end

Instance Method Details

#forget(ids:, reason: nil) ⇒ Object

Forgetting a knowledge graph is not a simple delete-by-id: edges are soft-archived and nodes can be left orphaned. A dedicated graph edge/node lifecycle (with orphan handling) is a deliberate follow-up.

Raises:

  • (NotImplementedError) 


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# File 'lib/llmemory/long_term/graph_based/memory.rb', line 127

def forget(ids:, reason: nil)
  raise NotImplementedError,
    "Graph forget is not implemented yet; edge/node lifecycle (archival + orphan handling) is a follow-up."
end

#list(user_id: nil, limit: nil) ⇒ Object 



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# File 'lib/llmemory/long_term/graph_based/memory.rb', line 115

def list(user_id: nil, limit: nil)
  @graph_storage.list_nodes(user_id || @user_id, limit: limit)
end

#memorize(conversation_text) ⇒ Object 



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# File 'lib/llmemory/long_term/graph_based/memory.rb', line 27

def memorize(conversation_text)
  text = Llmemory.configuration.noise_filter_enabled ? NoiseFilter.filter?(conversation_text) : conversation_text.to_s
  return true if text.strip.empty?

  data = @extractor.extract(text) rescue { entities: [], relations: [] }
  data = { entities: [], relations: [] } unless data.is_a?(Hash)
  entities = Array(data[:entities] || data["entities"])
  relations = Array(data[:relations] || data["relations"])

  return true if entities.empty? && relations.empty?

  provenance = Llmemory::Provenance.from_text_fingerprint(text, method: "entity_relation_extraction")
  name_to_id = {}

  entities.each do |e|
    next unless e.is_a?(Hash)
    entity_type = e[:type] || e["type"] || "concept"
    name = e[:name] || e["name"]
    next if name.nil? || name.to_s.strip.empty?
    id = @kg.add_node(entity_type: entity_type, name: name.to_s.strip, properties: { "provenance" => provenance })
    name_to_id[name.to_s.strip] ||= id
  end

  relations.each do |r|
    next unless r.is_a?(Hash)
    subject = (r[:subject] || r["subject"]).to_s.strip
    predicate = (r[:predicate] || r["predicate"]).to_s.strip
    object = (r[:object] || r["object"]).to_s.strip
    next if subject.empty? || predicate.empty? || object.empty?

    subject_id = name_to_id[subject] || @kg.add_node(entity_type: "concept", name: subject, properties: { "provenance" => provenance })
    object_id = name_to_id[object] || @kg.add_node(entity_type: "concept", name: object, properties: { "provenance" => provenance })

    edge = Edge.new(
      id: nil,
      user_id: @user_id,
      subject_id: subject_id,
      predicate: predicate,
      target_id: object_id,
      properties: { "provenance" => provenance },
      created_at: Time.now,
      archived_at: nil
    )
    @conflict_resolver.resolve(edge)
    edge_id = @kg.add_edge(subject: subject_id, predicate: predicate, object: object_id, properties: { "provenance" => provenance })

    text = "#{subject} #{predicate} #{object}"
    embedding = @vector_store.respond_to?(:embed) ? @vector_store.embed(text) : nil
    if embedding && @vector_store.respond_to?(:store)
      @vector_store.store(id: "edge_#{edge_id}", embedding: embedding, metadata: { text: text, created_at: Time.now }, user_id: @user_id)
    end
  end

  true
end

#retrieve(query, top_k: 10) ⇒ Object 



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# File 'lib/llmemory/long_term/graph_based/memory.rb', line 83

def retrieve(query, top_k: 10)
  results = hybrid_search(query, top_k: top_k)
  format_as_context(results)
end

#search_candidates(query, user_id: nil, top_k: 20) ⇒ Object 



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# File 'lib/llmemory/long_term/graph_based/memory.rb', line 88

def search_candidates(query, user_id: nil, top_k: 20)
  uid = user_id || @user_id
  return [] unless uid == @user_id
  results = hybrid_search(query, top_k: top_k)
  results.map do |r|
    {
      id: r[:id],
      text: r[:text],
      timestamp: r[:created_at] || r[:timestamp],
      score: r[:score] || 1.0,
      importance: r[:importance]
    }
  end
end

#stats(user_id: nil) ⇒ Object 



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# File 'lib/llmemory/long_term/graph_based/memory.rb', line 119

def stats(user_id: nil)
  uid = user_id || @user_id
  { nodes: @graph_storage.count_nodes(uid), edges: @graph_storage.count_edges(uid) }
end

#storage ⇒ Object 



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# File 'lib/llmemory/long_term/graph_based/memory.rb', line 105

def storage
  @graph_storage
end

#write(payload, **_meta) ⇒ Object

— MemoryModule uniform interface —



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# File 'lib/llmemory/long_term/graph_based/memory.rb', line 111

def write(payload, **_meta)
  memorize(payload)
end