Semantic Search over Text with NeuronDB (www.pgelephant.com)

NeuronDB is a new PostgreSQL extension that brings end-to-end semantic search inside your database, removing the need for separate vector stores or external ML services. It enables SQL-native workflows for embedding generation, storage, indexing and similarity search — useful for knowledge bases, support systems, and retrieval-augmented generation (RAG) pipelines. By keeping vectors and indexes in Postgres (compatible with PG 16–18) you reduce system complexity, simplify data governance, and speed up query latency for production workloads. Technically, NeuronDB introduces a native vector(n) column type (common dims: 384, 768, 1024), an embed_text(text, model) function (default: sentence-transformers/all-MiniLM-L6-v2), and optimized distance operators (<=> for cosine, <-> for L2). It supports HNSW for sub-10ms approximate nearest-neighbor queries at scale and IVFFlat for memory-efficient large collections. The guide covers schema design, chunking strategies, SQL examples for ingestion/updates, hybrid search and RAG integration, and indexing tips. For teams that want to build semantic search with familiar SQL tooling, NeuronDB promises production-ready performance and a much simpler architecture than stitching together separate DBs and vector services.