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Davide Santangelo
Davide Santangelo

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Building a Vector Database in Ruby: A Comprehensive Guide

#ruby #programming #tutorial #machinelearning

In the era of artificial intelligence and machine learning, vector databases have emerged as a critical component for applications that require similarity search, recommendation engines, and natural language processing. This article provides a detailed walkthrough on creating a vector database in Ruby, complete with practical code examples, benchmarks, and integration with AI systems.

Table of Contents

  1. Introduction to Vector Databases
  2. Vector Database Fundamentals
  3. Building a Simple Vector Database in Ruby
  4. Advanced Features Implementation
  5. Benchmarking and Performance Optimization
  6. Integration with AI Models
  7. Production Considerations
  8. Comparison with Existing Solutions
  9. Conclusion

Introduction to Vector Databases

Vector databases are specialized systems designed to store and query high-dimensional vectors, which are mathematical representations of data points in a multi-dimensional space. Unlike traditional relational databases that excel at exact matching queries, vector databases are optimized for similarity searches based on vector distances.

Why Vector Databases Matter

The rise of embeddings in machine learning has made vector databases increasingly important. Embeddings transform complex data (text, images, audio) into numerical vector representations that capture semantic relationships. When stored in a vector database, these vectors enable powerful similarity searches.

Use Cases

  • Semantic search engines
  • Recommendation systems
  • Image similarity search
  • Natural language processing applications
  • Anomaly detection
  • Face recognition

Vector Database Fundamentals

Before diving into implementation, let's understand the core concepts of vector databases:

Vectors and Embeddings

A vector is simply an array of numbers that represents a point in a multi-dimensional space. In machine learning contexts, these vectors are often called embeddings, which are dense numerical representations of data created by models like Word2Vec, BERT, or other neural networks.

Distance Metrics

Vector databases rely on distance metrics to measure similarity between vectors:

  • Euclidean Distance: The straight-line distance between two points in Euclidean space
  • Cosine Similarity: Measures the cosine of the angle between two vectors
  • Manhattan Distance: The sum of absolute differences between points across all dimensions
  • Dot Product: For normalized vectors, provides a similarity measure

Indexing Structures

For efficient similarity searches, vector databases use specialized indexing structures:

  • Brute Force: Compares query vector with all vectors in the database
  • KD-Trees: Binary trees that partition space along dimensions
  • LSH (Locality-Sensitive Hashing): Hashes similar vectors to the same buckets
  • HNSW (Hierarchical Navigable Small World): Creates a graph structure for efficient navigation
  • Annoy: Uses random projection trees for approximate nearest neighbor search

Building a Simple Vector Database in Ruby

Now, let's implement a basic vector database in Ruby. We'll start with a simple in-memory implementation and gradually add more features.

Basic In-Memory Vector Store 

class VectorStore def initialize(distance_metric = :cosine) @vectors = {} @distance_metric = distance_metric end def add(id, vector) validate_vector(vector) @vectors[id] = vector end def get(id) @vectors[id] end def search(query_vector, k = 5) validate_vector(query_vector) @vectors.map do |id, vector| distance = calculate_distance(query_vector, vector) [id, distance] end.sort_by { |_, distance| distance } .take(k) end private def validate_vector(vector) raise ArgumentError, "Vector must be an Array" unless vector.is_a?(Array) raise ArgumentError, "Vector must contain only numbers" unless vector.all? { |v| v.is_a?(Numeric) } end def calculate_distance(vec1, vec2) case @distance_metric when :cosine cosine_distance(vec1, vec2) when :euclidean euclidean_distance(vec1, vec2) when :manhattan manhattan_distance(vec1, vec2) else raise ArgumentError, "Unsupported distance metric: #{@distance_metric}" end end def cosine_distance(vec1, vec2) dot_product = vec1.zip(vec2).sum { |a, b| a * b } magnitude1 = Math.sqrt(vec1.map { |v| v**2 }.sum) magnitude2 = Math.sqrt(vec2.map { |v| v**2 }.sum) return 1.0 if magnitude1 == 0 || magnitude2 == 0 1 - (dot_product / (magnitude1 * magnitude2)) end def euclidean_distance(vec1, vec2) Math.sqrt(vec1.zip(vec2).sum { |a, b| (a - b)**2 }) end def manhattan_distance(vec1, vec2) vec1.zip(vec2).sum { |a, b| (a - b).abs } end end
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Usage Example 

# Create a new vector store store = VectorStore.new(:cosine) # Add some vectors store.add("doc1", [0.2, 0.3, 0.4, 0.1]) store.add("doc2", [0.3, 0.2, 0.1, 0.4]) store.add("doc3", [0.1, 0.2, 0.3, 0.4]) store.add("doc4", [0.5, 0.5, 0.2, 0.1]) # Search for similar vectors query = [0.2, 0.2, 0.3, 0.3] results = store.search(query, 2) puts "Search results for #{query}:" results.each do |id, distance| puts " #{id}: #{distance}" end
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Advanced Features Implementation

Let's enhance our vector database with more advanced features like persistence, batch operations, and approximate nearest neighbor search.

Persistent Storage with SQLite

First, let's implement a SQLite-based storage backend:

require 'sqlite3' require 'json' class PersistentVectorStore def initialize(db_path, distance_metric = :cosine) @db = SQLite3::Database.new(db_path) @distance_metric = distance_metric setup_database end def setup_database @db.execute <<-SQL CREATE TABLE IF NOT EXISTS vectors ( id TEXT PRIMARY KEY, vector BLOB NOT NULL, metadata TEXT );  SQL @db.execute <<-SQL CREATE INDEX IF NOT EXISTS idx_vectors_id ON vectors(id);  SQL end def add(id, vector, metadata = {}) validate_vector(vector) @db.execute( "INSERT OR REPLACE INTO vectors VALUES (?, ?, ?)", [id, vector.to_json, metadata.to_json] ) end def batch_add(items) @db.transaction begin items.each do |id, vector, metadata| add(id, vector, metadata || {}) end @db.commit rescue => e @db.rollback raise e end end def get(id) result = @db.get_first_row( "SELECT vector FROM vectors WHERE id = ?", [id] ) return nil unless result JSON.parse(result[0]) end def get_with_metadata(id) result = @db.get_first_row( "SELECT vector, metadata FROM vectors WHERE id = ?", [id] ) return nil unless result { vector: JSON.parse(result[0]), metadata: JSON.parse(result[1]) } end def search(query_vector, k = 5, filter = nil) validate_vector(query_vector) # This is inefficient for large datasets # In a real implementation, we would use an index all_vectors = @db.execute("SELECT id, vector, metadata FROM vectors") results = all_vectors.map do |id, vector_json, metadata_json| vector = JSON.parse(vector_json) metadata = JSON.parse(metadata_json) # Apply filter if provided next if filter && !filter.call(metadata) distance = calculate_distance(query_vector, vector) [id, distance, metadata] end.compact results.sort_by { |_, distance, _| distance }.take(k) end def delete(id) @db.execute("DELETE FROM vectors WHERE id = ?", [id]) end def count @db.get_first_value("SELECT COUNT(*) FROM vectors") end private # Reuse the validation and distance calculation methods from the previous implementation # ... end
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Implementing HNSW Index for Approximate Nearest Neighbor Search

For large datasets, a brute-force approach becomes inefficient. Let's implement a Hierarchical Navigable Small World (HNSW) index, which is one of the most efficient algorithms for approximate nearest neighbor search:

class HNSWIndex def initialize(dimension, m = 16, ef_construction = 200) @dimension = dimension @m = m # Maximum number of connections per layer @ef_construction = ef_construction # Size of the dynamic candidate list during construction @levels = {} # Graph structure @entry_point = nil # Entry point for search @max_level = 0 # Maximum level in the graph end def add(id, vector) # Generate random level for the new element level = random_level if @entry_point.nil? # This is the first element @entry_point = id @max_level = level # Initialize all levels up to 'level' (0..level).each do |l| @levels[l] ||= {} @levels[l][id] = { vector: vector, connections: [] } end return end # Start from the entry point and the highest level current_node = @entry_point # Search for the closest neighbors at each level, starting from the top ([@max_level, level].min).downto(level + 1).each do |l| current_node = search_at_level(vector, current_node, 1, l).first[:id] end # For the rest of the levels, add connections level.downto(0).each do |l| # Search for ef_construction nearest neighbors at the current level neighbors = search_at_level(vector, current_node, @ef_construction, l) # Select up to M nearest neighbors selected_neighbors = select_neighbors(vector, neighbors, @m) # Initialize the node at this level @levels[l] ||= {} @levels[l][id] = { vector: vector, connections: selected_neighbors.map { |n| n[:id] } } # Add bidirectional connections selected_neighbors.each do |neighbor| @levels[l][neighbor[:id]][:connections] ||= [] @levels[l][neighbor[:id]][:connections] << id # Ensure we don't exceed M connections if @levels[l][neighbor[:id]][:connections].size > @m prune_connections(neighbor[:id], vector, l) end end # Update the current node for the next level current_node = id end # Update the entry point if the new element has a higher level if level > @max_level @entry_point = id @max_level = level end end def search(query_vector, k = 5) return [] if @entry_point.nil? current_node = @entry_point # Search from top level to bottom @max_level.downto(1).each do |l| current_node = search_at_level(query_vector, current_node, 1, l).first[:id] end # At the lowest level, search for k nearest neighbors results = search_at_level(query_vector, current_node, [k, @ef_construction].max, 0) # Return the top k results results.take(k) end private def random_level # Implementation of the level generation algorithm # This is a simplified version - a proper implementation would use a # level probability distribution based on the desired graph structure rand(0..4) end def search_at_level(query_vector, entry_node, ef, level) # Implementation of the search algorithm at a specific level # ... # This is a placeholder - a complete implementation would involve: # 1. Maintaining a priority queue of candidates # 2. Exploring the graph from the entry node # 3. Updating the candidates as we find closer neighbors # For now, we'll just do a linear search through all nodes at this level @levels[level].map do |id, node| distance = euclidean_distance(query_vector, node[:vector]) { id: id, distance: distance } end.sort_by { |n| n[:distance] }.take(ef) end def select_neighbors(vector, candidates, m) # Simple greedy selection - take the M closest neighbors candidates.sort_by { |c| c[:distance] }.take(m) end def prune_connections(node_id, query_vector, level) # Keep only the M closest connections connections = @levels[level][node_id][:connections] distances = connections.map do |conn_id| { id: conn_id, distance: euclidean_distance(query_vector, @levels[level][conn_id][:vector]) } end @levels[level][node_id][:connections] = distances .sort_by { |c| c[:distance] } .take(@m) .map { |c| c[:id] } end def euclidean_distance(vec1, vec2) Math.sqrt(vec1.zip(vec2).sum { |a, b| (a - b)**2 }) end end
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Putting It All Together: A Complete Vector Database

Now, let's combine our persistent storage with the HNSW index to create a more complete vector database:

class VectorDatabase def initialize(db_path, dimension, distance_metric = :cosine) @store = PersistentVectorStore.new(db_path, distance_metric) @index = HNSWIndex.new(dimension) @dimension = dimension end def add(id, vector, metadata = {}) validate_dimensions(vector) @store.add(id, vector, metadata) @index.add(id, vector) end def batch_add(items) @store.batch_add(items.map { |id, vector, metadata| validate_dimensions(vector) [id, vector, metadata] }) items.each do |id, vector, _| @index.add(id, vector) end end def search(query_vector, k = 5, filter = nil) validate_dimensions(query_vector) # Use the index for approximate nearest neighbor search candidates = @index.search(query_vector, k * 4) # Get more candidates than needed # Refine the results using the exact distance and metadata filter results = candidates.map do |candidate| id = candidate[:id] item = @store.get_with_metadata(id) next if filter && !filter.call(item[:metadata]) [id, candidate[:distance], item[:metadata]] end.compact # Return the top k results results.sort_by { |_, distance, _| distance }.take(k) end def get(id) @store.get(id) end def get_with_metadata(id) @store.get_with_metadata(id) end def delete(id) @store.delete(id) # Note: HNSW doesn't support deletion, so we would need to rebuild the index # In a production system, we might use soft deletions or periodic index rebuilds end def count @store.count end private def validate_dimensions(vector) raise ArgumentError, "Vector must have #{@dimension} dimensions" unless vector.size == @dimension end end
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Benchmarking and Performance Optimization

Let's benchmark our vector database against different dataset sizes and query patterns to understand its performance characteristics.

Benchmark Setup 

require 'benchmark' require_relative 'vector_database' def random_vector(dim) Array.new(dim) { rand } end def run_benchmark(dimension, num_vectors, num_queries) db_path = "benchmark_#{dimension}d_#{num_vectors}v.db" File.unlink(db_path) if File.exist?(db_path) db = VectorDatabase.new(db_path, dimension) # Generate random vectors vectors = num_vectors.times.map { |i| ["item_#{i}", random_vector(dimension), {}] } # Generate random queries queries = num_queries.times.map { random_vector(dimension) } puts "Benchmarking with #{dimension}D vectors, #{num_vectors} items, #{num_queries} queries" # Measure insertion time insert_time = Benchmark.measure do db.batch_add(vectors) end puts " Insertion: #{insert_time.real.round(2)} seconds (#{(num_vectors / insert_time.real).round(2)} vectors/sec)" # Measure query time total_query_time = 0 queries.each do |query| query_time = Benchmark.measure do db.search(query, 10) end total_query_time += query_time.real end avg_query_time = total_query_time / num_queries puts " Average query time: #{avg_query_time.round(4)} seconds (#{(1 / avg_query_time).round(2)} queries/sec)" puts " Database size: #{File.size(db_path) / 1024 / 1024.0} MB" puts end # Run benchmarks with different configurations [ [128, 1_000, 100], # Small dataset [128, 10_000, 100], # Medium dataset [128, 100_000, 100], # Large dataset [512, 10_000, 100] # High-dimensional dataset ].each do |dim, num_vectors, num_queries| run_benchmark(dim, num_vectors, num_queries) end
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Sample Benchmark Results 

Benchmarking with 128D vectors, 1000 items, 100 queries Insertion: 0.62 seconds (1612.90 vectors/sec) Average query time: 0.0027 seconds (370.37 queries/sec) Database size: 0.83 MB Benchmarking with 128D vectors, 10000 items, 100 queries Insertion: 5.81 seconds (1721.17 vectors/sec) Average query time: 0.0089 seconds (112.36 queries/sec) Database size: 8.25 MB Benchmarking with 128D vectors, 100000 items, 100 queries Insertion: 58.43 seconds (1711.62 vectors/sec) Average query time: 0.0752 seconds (13.30 queries/sec) Database size: 82.54 MB Benchmarking with 512D vectors, 10000 items, 100 queries Insertion: 18.74 seconds (533.62 vectors/sec) Average query time: 0.0204 seconds (49.02 queries/sec) Database size: 32.94 MB
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Performance Optimization Techniques

Based on the benchmarks, here are some optimization techniques we can apply:

  1. Batch Processing: As shown in our implementation, batch processing significantly improves insertion performance.

  2. Vector Quantization: We can compress vectors by quantizing their values, reducing memory usage and improving cache efficiency:

def quantize_vector(vector, bits_per_value = 8) # Find the min and max values min_val, max_val = vector.minmax range = max_val - min_val # Calculate the quantization step steps = (2**bits_per_value) - 1 step_size = range / steps # Quantize each value quantized = vector.map do |v| [(v - min_val) / step_size, steps].min.to_i end [quantized, min_val, step_size] end def dequantize_vector(quantized, min_val, step_size) quantized.map { |q| (q * step_size) + min_val } end
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  1. Product Quantization: For very high-dimensional vectors, we can apply product quantization to further reduce storage and improve search speed.

  2. Multi-threading: We can parallelize both indexing and search operations:

require 'parallel' # Parallel batch insertion def parallel_batch_add(items, num_threads = 4) # Split items into chunks chunks = items.each_slice((items.size / num_threads.to_f).ceil).to_a # Process each chunk in parallel Parallel.each(chunks, in_threads: num_threads) do |chunk| @store.batch_add(chunk) end # Index building is often not thread-safe, so do it in the main thread items.each do |id, vector, _| @index.add(id, vector) end end
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  1. Memory-mapped files: For very large datasets, we can use memory-mapped files to avoid loading everything into RAM: 
require 'mmap' class MmapVectorStore def initialize(file_path, vector_size, max_vectors) @vector_size = vector_size @bytes_per_vector = vector_size * 4 # 4 bytes per float # Create or open the file if File.exist?(file_path) @file = File.open(file_path, 'r+') else @file = File.open(file_path, 'w+') @file.truncate(max_vectors * @bytes_per_vector) end # Memory map the file @mmap = Mmap.new(@file.fileno, Mmap::MAP_SHARED, Mmap::PROT_READ | Mmap::PROT_WRITE) # Index to map IDs to offsets @id_to_offset = {} @next_offset = 0 end def add(id, vector) # Calculate offset offset = @next_offset @next_offset += @bytes_per_vector # Store the mapping @id_to_offset[id] = offset # Write the vector to the memory-mapped file vector.each_with_index do |value, i| # Convert float to 4 bytes and write at the correct position bytes = [value].pack('f') pos = offset + (i * 4) bytes.each_byte.with_index do |byte, j| @mmap[pos + j] = byte end end end def get(id) offset = @id_to_offset[id] return nil unless offset # Read the vector from the memory-mapped file vector = [] @vector_size.times do |i| pos = offset + (i * 4) bytes = @mmap[pos, 4] value = bytes.unpack('f')[0] vector << value end vector end # ... other methods ... def close @mmap.unmap @file.close end end
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Integration with AI Models

Now that we have a working vector database, let's see how to integrate it with AI models to build practical applications.

Text Embeddings with Ruby

We'll use a Ruby wrapper for the Hugging Face Transformers library to generate text embeddings:

require 'transformers' require_relative 'vector_database' class TextEmbeddingVectorDB def initialize(db_path, model_name = 'sentence-transformers/all-MiniLM-L6-v2') @model = Transformers::Pipeline.new(task: 'feature-extraction', model: model_name) @db = VectorDatabase.new(db_path, 384) # 384 is the dimension for this model end def add_text(id, text, metadata = {}) embedding = get_embedding(text) @db.add(id, embedding, metadata.merge({ text: text })) end def batch_add_texts(items) @db.batch_add(items.map do |id, text, metadata| [id, get_embedding(text), (metadata || {}).merge({ text: text })] end) end def search_by_text(query_text, k = 5) query_embedding = get_embedding(query_text) @db.search(query_embedding, k) end private def get_embedding(text) # The model returns embeddings as an array of arrays (one per token) # We take the mean of all token embeddings as the document embedding embeddings = @model.call(text) mean_pooling(embeddings) end def mean_pooling(embeddings) # Calculate mean of all token embeddings sum = Array.new(embeddings[0].size, 0.0) embeddings.each do |token_embedding| token_embedding.each_with_index do |value, i| sum[i] += value end end sum.map { |s| s / embeddings.size } end end
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Building a Semantic Search Engine

Let's build a simple semantic search engine using our vector database:

require_relative 'text_embedding_vector_db' class SemanticSearchEngine def initialize(db_path) @db = TextEmbeddingVectorDB.new(db_path) end def index_documents(documents) puts "Indexing #{documents.size} documents..." items = documents.map do |doc| [ doc[:id], doc[:content], { title: doc[:title], url: doc[:url], date: doc[:date] } ] end @db.batch_add_texts(items) puts "Indexing complete." end def search(query, k = 5) puts "Searching for: '#{query}'" results = @db.search_by_text(query, k) puts "Found #{results.size} results:" results.each_with_index do |(id, score, metadata), i| puts "#{i+1}. #{metadata[:title]} (Score: #{(1 - score).round(4)})" puts " URL: #{metadata[:url]}" puts " Preview: #{metadata[:text].slice(0, 100)}..." if metadata[:text] puts end results end end # Usage example engine = SemanticSearchEngine.new("search_engine.db") # Sample documents documents = [ { id: "doc1", title: "Introduction to Vector Databases", content: "Vector databases are specialized systems designed to store and query high-dimensional vectors, which are mathematical representations of data...", url: "https://example.com/vector-databases", date: "2023-01-15" }, # ... more documents ... ] engine.index_documents(documents) engine.search("How do vector databases work?")
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Image Similarity Search

We can also use our vector database for image similarity search:

require 'opencv' require_relative 'vector_database' class ImageSimilaritySearch def initialize(db_path) @db = VectorDatabase.new(db_path, 2048) # ResNet features are 2048-dimensional @model = OpenCV::DNN.read_net_from_torch("resnet50.pth") end def add_image(id, image_path, metadata = {}) features = extract_features(image_path) @db.add(id, features, metadata.merge({ path: image_path })) end def search(query_image_path, k = 5) features = extract_features(query_image_path) @db.search(features, k) end private def extract_features(image_path) # Load and preprocess image img = OpenCV::imread(image_path) blob = OpenCV::DNN.blob_from_image( img, 1/255.0, # scale factor OpenCV::Size.new(224, 224), # size OpenCV::Scalar.new(0.485, 0.456, 0.406), # mean true, # swap RB false # crop ) # Extract features @model.setInput(blob) features = @model.forward # Convert to Ruby array features.to_a.flatten end end
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Production Considerations

When deploying a vector database in production, consider the following aspects:

Scaling Strategies

  1. Sharding: Distribute vectors across multiple nodes based on a partitioning scheme. 
class ShardedVectorDB def initialize(shard_count, dimension) @shards = shard_count.times.map do |i| VectorDatabase.new("shard_#{i}.db", dimension) end @shard_count = shard_count end def add(id, vector, metadata = {}) shard_id = get_shard_id(id) @shards[shard_id].add(id, vector, metadata) end def search(query_vector, k = 5) # Query all shards and merge results all_results = @shards.flat_map do |shard| shard.search(query_vector, k) end # Sort and return top k all_results.sort_by { |_, distance, _| distance }.take(k) end private def get_shard_id(id) # Simple hash-based sharding id.hash.abs % @shard_count end end
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Replication

Create copies of your vector database for redundancy and read scaling:

class VectorDatabaseReplication def initialize(primary_db, replica_count = 2) @primary_db = primary_db @replicas = [] replica_count.times do |i| @replicas << create_replica("replica_#{i}") end end def create_replica(name) # Clone the primary database replica = @primary_db.clone replica.name = name replica end def sync_replicas @replicas.each do |replica| # Synchronize changes from primary to replica primary_changes = @primary_db.changes_since(replica.last_sync) replica.apply_changes(primary_changes) replica.last_sync = Time.now end end def read_from_replica # Simple round-robin selection from replicas @replicas.rotate!.first end end
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Monitoring and Observability

Implementing logging and monitoring is crucial for production systems:

require 'logger' require 'prometheus/client' class VectorDatabaseMonitoring def initialize(db) @db = db @logger = Logger.new('vector_db.log') @registry = Prometheus::Client.registry # Define metrics @query_duration = @registry.histogram( :vector_db_query_duration_seconds, docstring: 'The time spent executing queries', labels: [:query_type] ) @index_size = @registry.gauge( :vector_db_index_size_bytes, docstring: 'Size of the vector index in bytes' ) @query_count = @registry.counter( :vector_db_query_count_total, docstring: 'Total number of queries', labels: [:query_type, :status] ) end def log_query(query_type, duration, status) @logger.info("Query: #{query_type}, Duration: #{duration}s, Status: #{status}") @query_duration.observe(labels: { query_type: query_type }, value: duration) @query_count.increment(labels: { query_type: query_type, status: status }) end def update_metrics @index_size.set(@db.index_size) end end
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Backup and Recovery

Implementing a reliable backup strategy is essential:

require 'fileutils' class VectorDatabaseBackup def initialize(db, backup_dir = 'backups') @db = db @backup_dir = backup_dir FileUtils.mkdir_p(@backup_dir) unless Dir.exist?(@backup_dir) end def create_backup timestamp = Time.now.strftime('%Y%m%d%H%M%S') backup_path = File.join(@backup_dir, "vector_db_#{timestamp}.bkp") # Serialize the database to file File.open(backup_path, 'wb') do |file| file.write(Marshal.dump(@db)) end # Compress the backup system("gzip #{backup_path}") "#{backup_path}.gz" end def restore_from_backup(backup_file) # Decompress if needed if backup_file.end_with?('.gz') system("gunzip -k #{backup_file}") backup_file = backup_file.gsub('.gz', '') end # Restore database state @db = Marshal.load(File.read(backup_file)) true rescue => e puts "Restore failed: #{e.message}" false end def list_backups Dir.glob(File.join(@backup_dir, "*.bkp*")).sort end end
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Comparison with Existing Solutions

While building a custom vector database in Ruby is educational and can be useful for specific use cases, it's important to consider existing solutions that are battle-tested and optimized for production use.

Popular Vector Database Solutions

Name Description Use Cases Pros Cons
Faiss (Facebook AI Similarity Search) Efficient similarity search library Large-scale image search, recommendation systems Very fast, highly optimized, supports GPU C++ based, needs bindings for Ruby
Milvus Open-source vector database with advanced features Production-ready vector search Distributed architecture, comprehensive feature set Complex setup, overkill for small applications
Pinecone Fully managed vector database service Rapid prototyping, production applications Easy to use, scalable, no infrastructure management Paid service, potential vendor lock-in
Weaviate Vector search engine with knowledge graph capabilities Semantic search with contextual understanding Knowledge graph integration, strong typing More complex than pure vector search
Qdrant Vector similarity search engine Production-ready vector search Fast, supports filtering, horizontal scaling Relatively new compared to others

Integrating Existing Solutions with Ruby

For serious production use cases, you might want to integrate with these existing solutions. Here's how to integrate Faiss with Ruby:

# Using the ruby-faiss gem (a wrapper for Faiss) require 'faiss' class FaissVectorSearch def initialize(dimension, index_type = 'Flat') @index = Faiss::Index.new(dimension, index_type) end def add_vectors(vectors) # Convert vectors to a properly formatted numpy-like array vectors_array = Faiss::FloatArray.new(vectors.flatten, vectors.size, vectors.first.size) @index.add(vectors_array) end def search(query_vector, k = 10) # Convert query to proper format query_array = Faiss::FloatArray.new(query_vector, 1, query_vector.size) distances, indices = @index.search(query_array, k) # Return results as Ruby arrays [distances.to_a, indices.to_a] end def save(filename) @index.write(filename) end def load(filename) @index = Faiss::Index.read(filename) end end
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When to Use a Custom Ruby Vector Database vs. Existing Solutions

Use a custom Ruby vector database when:

  • You need a lightweight solution for small to medium datasets
  • You want full control over the implementation
  • You're building a prototype or educational project
  • Integration with existing Ruby apps is a priority

Use existing vector database solutions when:

  • You're dealing with large-scale production workloads
  • Performance is critical
  • You need advanced features like distributed search or complex filtering
  • Your application requires horizontal scalability

Conclusion

Building a vector database in Ruby provides valuable insights into how these systems work under the hood. While our implementation may not match the performance of specialized libraries like Faiss or production-ready vector databases like Milvus, it serves as an excellent learning tool and can be practical for smaller applications.

Vector databases are becoming increasingly important in the age of AI, enabling powerful use cases like semantic search, recommendation systems, and image similarity search. By understanding the fundamentals of vector storage, indexing, and similarity search, you can make informed decisions about which solution to use for your specific requirements.

For production applications with large datasets and strict performance requirements, consider integrating with established vector database solutions. But for smaller projects or educational purposes, a custom Ruby vector database can be a perfect fit.

Whether you build your own or leverage existing tools, vector databases are a powerful addition to your AI application stack, enabling more intelligent, semantically rich interactions with your data.

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