Graph-based approximate nearest neighbor search in Postgres
Neon is pleased to announce the release of our new pg_embedding extension, which enables using the Hierarchical Navigable Small World (HNSW) algorithm for graph-based approximate nearest neighbor search in Postgres and LangChain.
The pg_embedding extension increases speed by up to 20x for 99% accuracy for approximate nearest neighbor search compared to pgvector.
Implementing pg_embedding in your application involves running a few simple SQL statements. Prior knowledge of vector indexes is optional. To learn more, read the blog post, refer to the pg_embedding documentation, or checkout the AI page on our website.
Fixes & improvements
Proxy: The wake-up logic for compute nodes was updated to reduce the number of errors returned to clients attempting to connect to Neon. Wake-up logic now supports quicker retries and will skip a connection attempt if failure is expected. Additionally, a 100ms sleep interval and IO error handling were introduced to manage scenarios in which compute nodes are not yet available as they wait for a Kubernetes DNS to be propagated.