pgvector is a PostgreSQL extension and Ruby gem that enables vector similarity search directly in your Rails database.
It is particularly useful for AI-powered applications that rely on embeddings, such as semantic search, recommendations, and classification. Setup involves installing the PostgreSQL extension, running a Rails migration to add vector columns, and then using ActiveRecord queries with the cosine_distance or inner_product functions.
The gem is well-documented and actively maintained by Andrew Kane, with support available on GitHub and a strong user base. Its strengths are its performance, simplicity, and ability to avoid external vector databases. Weaknesses include reliance on PostgreSQL-specific features and limited scalability for extremely large datasets compared to specialized vector databases.
Alternatives include using Pinecone, Weaviate, or Milvus for larger-scale semantic search workloads.
It is particularly useful for AI-powered applications that rely on embeddings, such as semantic search, recommendations, and classification. Setup involves installing the PostgreSQL extension, running a Rails migration to add vector columns, and then using ActiveRecord queries with the cosine_distance or inner_product functions.
The gem is well-documented and actively maintained by Andrew Kane, with support available on GitHub and a strong user base. Its strengths are its performance, simplicity, and ability to avoid external vector databases. Weaknesses include reliance on PostgreSQL-specific features and limited scalability for extremely large datasets compared to specialized vector databases.
Alternatives include using Pinecone, Weaviate, or Milvus for larger-scale semantic search workloads.