Building a Vector Database in Ruby: A Comprehensive Guide

This article details building a vector database in Ruby, focusing on similarity search capabilities crucial for AI applications. It begins by introducing vector databases and their importance in handling high-dimensional data for similarity searches, particularly with the rise of embeddings in machine learning. Key concepts like vectors, embeddings, and distance metrics (Euclidean, cosine, Manhattan) are explained, along with indexing structures (brute force, KD-trees, LSH, HNSW, Annoy). A basic in-memory Ruby implementation is presented, demonstrating vector addition and similarity search. The article then enhances this with persistent SQLite storage and batch operations for improved efficiency. Furthermore, it integrates an approximate nearest neighbor search using the HNSW algorithm to handle large datasets effectively. A complete vector database combining persistent storage and the HNSW index is constructed, showcasing a practical solution. The article concludes by consolidating the components into a robust, scalable vector database suitable for real-world applications. Code examples are provided throughout to illustrate the concepts and implementations. Performance optimization and production considerations are implicitly addressed through the choice of data structures and algorithms.