Class: Leann::Backend::LeannGraph

Inherits:
Object
  • Object
show all
Defined in:
lib/leann/backend/leann_graph.rb

Overview

LEANN Graph-only backend - stores only the HNSW graph structure Achieves ~97% storage reduction by not storing embeddings

Graph is stored as:

  • Node levels (which layers each node participates in)

  • Neighbor lists per level

  • Entry point and HNSW parameters

During search, embeddings are recomputed on-the-fly via API calls

Constant Summary collapse

DEFAULT_M =

HNSW parameters

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DEFAULT_EF_CONSTRUCTION =

Max connections per layer

200
DEFAULT_ML =

Build-time search width

1.0 / Math.log(DEFAULT_M)

Instance Attribute Summary collapse

Class Method Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(dimensions:, m: DEFAULT_M, ef_construction: DEFAULT_EF_CONSTRUCTION) ⇒ LeannGraph

Returns a new instance of LeannGraph.



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# File 'lib/leann/backend/leann_graph.rb', line 26

def initialize(dimensions:, m: DEFAULT_M, ef_construction: DEFAULT_EF_CONSTRUCTION)
  @dimensions = dimensions
  @m = m
  @m0 = m * 2  # Layer 0 has 2x connections
  @ef_construction = ef_construction
  @ml = 1.0 / Math.log(m)

  @nodes = []           # Array of node data {id:, level:}
  @id_to_idx = {}       # Map document ID to node index
  @neighbors = []       # neighbors[idx][level] = [neighbor_indices]
  @entry_point = nil
  @max_level = -1
  @node_count = 0
end

Instance Attribute Details

#dimensions ⇒ Object (readonly)

Returns the value of attribute dimensions.



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# File 'lib/leann/backend/leann_graph.rb', line 23

def dimensions
  @dimensions
end

#ef_construction ⇒ Object (readonly)

Returns the value of attribute ef_construction.



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# File 'lib/leann/backend/leann_graph.rb', line 23

def ef_construction
  @ef_construction
end

#entry_point ⇒ Object (readonly)

Returns the value of attribute entry_point.



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# File 'lib/leann/backend/leann_graph.rb', line 23

def entry_point
  @entry_point
end

#m ⇒ Object (readonly)

Returns the value of attribute m.



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# File 'lib/leann/backend/leann_graph.rb', line 23

def m
  @m
end

#max_level ⇒ Object (readonly)

Returns the value of attribute max_level.



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# File 'lib/leann/backend/leann_graph.rb', line 23

def max_level
  @max_level
end

#node_count ⇒ Object (readonly)

Returns the value of attribute node_count.



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# File 'lib/leann/backend/leann_graph.rb', line 24

def node_count
  @node_count
end

Class Method Details

.load(path) ⇒ LeannGraph

Load graph from files

Parameters:

  • path (String) —

    Base path for index files

Returns:

Raises:



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# File 'lib/leann/backend/leann_graph.rb', line 152

def self.load(path)
  graph_file = "#{path}.graph.bin"
  meta_file = "#{path}.graph.meta.json"

  raise IndexNotFoundError, "Graph file not found: #{graph_file}" unless File.exist?(graph_file)

  meta = JSON.parse(File.read(meta_file), symbolize_names: true)

  graph = new(
    dimensions: meta[:dimensions],
    m: meta[:m],
    ef_construction: meta[:ef_construction]
  )
  graph.load_from_files(path, meta)
  graph
end

Instance Method Details

#build(ids, embeddings) ⇒ self

Build the graph from embeddings After building, embeddings can be discarded

Parameters:

  • ids (Array<String>) —

    Document IDs

  • embeddings (Array<Array<Float>>) —

    Embedding vectors

Returns:

  • (self)

Raises:

  • (ArgumentError) 


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# File 'lib/leann/backend/leann_graph.rb', line 47

def build(ids, embeddings)
  raise ArgumentError, "IDs and embeddings must have same length" unless ids.length == embeddings.length
  return self if ids.empty?

  @node_count = ids.length
  puts "Building LEANN graph with #{@node_count} nodes (M=#{@m})..."

  ids.each_with_index do |id, idx|
    level = random_level
    @nodes << { id: id, level: level }
    @id_to_idx[id] = idx
    @neighbors << Array.new(level + 1) { [] }

    if @entry_point.nil?
      @entry_point = idx
      @max_level = level
    else
      # Insert node into graph
      insert_node(idx, embeddings[idx], embeddings, level)
    end

    print "." if (idx + 1) % 100 == 0
  end

  puts "\nGraph built: #{@node_count} nodes, max_level=#{@max_level}"
  self
end

#get_id(node_idx) ⇒ Object

Get document ID for a node index



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# File 'lib/leann/backend/leann_graph.rb', line 287

def get_id(node_idx)
  @nodes[node_idx][:id]
end

#get_idx(id) ⇒ Object

Get node index for a document ID



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# File 'lib/leann/backend/leann_graph.rb', line 292

def get_idx(id)
  @id_to_idx[id]
end

#get_neighbors(node_idx, level) ⇒ Object

Get neighbors at a specific level



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# File 'lib/leann/backend/leann_graph.rb', line 280

def get_neighbors(node_idx, level)
  return [] if node_idx >= @neighbors.length
  return [] if level >= @neighbors[node_idx].length
  @neighbors[node_idx][level] || []
end

#load_from_files(path, meta) ⇒ Object

Load graph data from binary file



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# File 'lib/leann/backend/leann_graph.rb', line 170

def load_from_files(path, meta)
  graph_file = "#{path}.graph.bin"

  @node_count = meta[:node_count]
  @max_level = meta[:max_level]
  @entry_point = meta[:entry_point]

  File.open(graph_file, "rb") do |f|
    # Read node count
    node_count = f.read(8).unpack1("Q<")
    raise "Node count mismatch" unless node_count == @node_count

    # Read levels
    levels = f.read(@node_count * 4).unpack("l<*")

    # Read node IDs
    @nodes = []
    @id_to_idx = {}
    @node_count.times do |idx|
      id_len = f.read(2).unpack1("S<")
      id = f.read(id_len).force_encoding("UTF-8")
      @nodes << { id: id, level: levels[idx] }
      @id_to_idx[id] = idx
    end

    # Read offsets
    offsets = f.read((@node_count + 1) * 8).unpack("Q<*")

    # Read level offsets
    level_offsets_count = f.read(8).unpack1("Q<")
    level_offsets = f.read(level_offsets_count * 8).unpack("Q<*")

    # Read all neighbors
    neighbors_count = f.read(8).unpack1("Q<")
    all_neighbors = f.read(neighbors_count * 4).unpack("l<*")

    # Reconstruct neighbor structure
    @neighbors = []
    level_offset_idx = 0
    @node_count.times do |idx|
      node_level = levels[idx]
      node_neighbors = []
      base_offset = offsets[idx]

      (node_level + 1).times do |level|
        start_off = level_offsets[level_offset_idx]
        end_off = level_offsets[level_offset_idx + 1]
        level_offset_idx += 1

        level_neighbors = all_neighbors[(base_offset + start_off)...(base_offset + end_off)]
        node_neighbors << (level_neighbors || [])
      end
      level_offset_idx += 1  # Skip end marker

      @neighbors << node_neighbors
    end
  end

  puts "Graph loaded: #{@node_count} nodes, max_level=#{@max_level}"
end

#save(path) ⇒ Object

Save graph to files (no embeddings!)

Parameters:

  • path (String) —

    Base path for index files



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# File 'lib/leann/backend/leann_graph.rb', line 78

def save(path)
  graph_file = "#{path}.graph.bin"
  meta_file = "#{path}.graph.meta.json"

  # Save metadata
  meta = {
    version: "1.0",
    format: "leann_graph",
    node_count: @node_count,
    dimensions: @dimensions,
    m: @m,
    ef_construction: @ef_construction,
    max_level: @max_level,
    entry_point: @entry_point
  }
  File.write(meta_file, JSON.pretty_generate(meta))

  # Save graph in binary format
  File.open(graph_file, "wb") do |f|
    # Header
    f.write([@node_count].pack("Q<"))  # uint64 node count

    # Node levels
    levels = @nodes.map { |n| n[:level] }
    f.write(levels.pack("l<*"))  # int32 array

    # Node IDs (as length-prefixed strings)
    @nodes.each do |node|
      id_bytes = node[:id].to_s.encode("UTF-8")
      f.write([id_bytes.bytesize].pack("S<"))  # uint16 length
      f.write(id_bytes)
    end

    # Neighbor lists (CSR-like format)
    # First: offsets into neighbor data for each node
    offsets = []
    current_offset = 0
    @neighbors.each do |node_neighbors|
      offsets << current_offset
      node_neighbors.each do |level_neighbors|
        current_offset += level_neighbors.length
      end
    end
    offsets << current_offset  # Final offset

    f.write(offsets.pack("Q<*"))  # uint64 array

    # Level offsets within each node
    level_offsets = []
    @neighbors.each do |node_neighbors|
      level_offset = 0
      node_neighbors.each do |level_neighbors|
        level_offsets << level_offset
        level_offset += level_neighbors.length
      end
      level_offsets << level_offset  # End marker for this node
    end
    f.write([level_offsets.length].pack("Q<"))
    f.write(level_offsets.pack("Q<*"))

    # All neighbor indices (flattened)
    all_neighbors = @neighbors.flat_map { |nn| nn.flat_map { |ln| ln } }
    f.write([all_neighbors.length].pack("Q<"))
    f.write(all_neighbors.pack("l<*"))  # int32 array
  end

  graph_size = File.size(graph_file)
  puts "Graph saved: #{format_bytes(graph_size)} (no embeddings stored!)"
end

#search(query_embedding, embedding_provider:, passages:, limit: 5, ef: nil) ⇒ Array<[String, Float]>

Search the graph using on-the-fly embedding computation

Parameters:

  • query_embedding (Array<Float>) —

    Query vector

  • embedding_provider (Embedding::Base) —

    For recomputing embeddings

  • passages (Hash) —

    id => text mapping for recomputation

  • limit (Integer) (defaults to: 5) —

    Number of results

  • ef (Integer) (defaults to: nil) —

    Search width (higher = more accurate, slower)

Returns:

  • (Array<[String, Float]>) —

    Array of [id, score] pairs



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# File 'lib/leann/backend/leann_graph.rb', line 239

def search(query_embedding, embedding_provider:, passages:, limit: 5, ef: nil)
  return [] if @entry_point.nil?

  ef ||= [limit * 2, 10].max

  # Cache for embeddings computed during this search
  embedding_cache = {}

  # Start from entry point, traverse down to layer 0
  current = @entry_point
  current_dist = distance(query_embedding, get_embedding(current, embedding_provider, passages, embedding_cache))

  # Greedy search from top layer down to layer 1
  (@max_level).downto(1) do |level|
    changed = true
    while changed
      changed = false
      neighbors = get_neighbors(current, level)
      neighbors.each do |neighbor|
        neighbor_emb = get_embedding(neighbor, embedding_provider, passages, embedding_cache)
        neighbor_dist = distance(query_embedding, neighbor_emb)
        if neighbor_dist < current_dist
          current = neighbor
          current_dist = neighbor_dist
          changed = true
        end
      end
    end
  end

  # Search layer 0 with ef-sized candidate set
  candidates = search_layer(query_embedding, current, ef, 0, embedding_provider, passages, embedding_cache)

  # Return top-k results, converted to similarity scores
  candidates
    .sort_by { |_, dist| dist }
    .first(limit)
    .map { |idx, dist| [@nodes[idx][:id], 1.0 - dist] }  # Convert distance to similarity
end