We are Neon, the serverless Postgres. We power thousands of AI apps with the pgvector extension and separate storage and compute enabling your database resources to scale independently. In this article, Raouf explains how you can use Neon’s elasticity, and parallel HNSW index build in pgvector (0.5.1 for now, and soon 0.6.0) to scale your […]

Materialize

Bobby Iliev

DevOps

Community Picks is a section on daily.dev where our community members share the most interesting and valuable content they've discovered online. From insightful articles to handy tools, every post is a gem curated by our dedicated coomunity. To contribute to Community Picks, you need to have at least 250 reputation points, ensuring that only active and trusted members can share their finds.

Community Picks

The new pgvector 0.6.0 extension for Postgres significantly improves HNSW index build times by up to 30x due to a parallel index build feature. With Neon's elastic capabilities, users can scale their database resources for efficient AI applications. pgvector is crucial for semantic search and Retrieval Augmented Generation (RAG) applications, enhancing the long-term memory of large language models. Key Postgres settings such as `maintenance_work_mem` and `max_parallel_maintenance_workers` can further optimize index build performance.

pgvector: 30x Faster Index Build for your Vector Embeddings

HNSW’s Achilles heel: memory and build time