Every RAG prototype works on twenty documents. Then the real corpus shows up with its real shape: maintenance procedures that cite API 610, drilling reports that name a PZ-7 mud pump, permits and work orders full of tag numbers. Ask a pure vector index for “PZ-7 liner torque” and it cheerfully returns five documents about […]

pg_partman 5.5.0 changed its background worker’s default role, and pg_cron 1.6.8 added Postgres 19 support. Here is how to run partition maintenance that does not go quiet.

Houston teams are past the pgvector demo stage. The prototype indexed a few hundred thousand embeddings, queries came back in single-digit milliseconds, and everyone moved on. Then the corpus grew — refinery maintenance manuals, upstream drilling reports, municipal permit records, or an e-discovery set — and the same index started losing recall, or the build […]

Embeddings search is no longer a novelty feature; it is table stakes for Postgres teams building RAG pipelines, semantic search, and recommendation engines. pgvector, the open-source extension that adds vector columns and nearest-neighbor search to PostgreSQL, is where most of those teams start. The current v0.8.x line supports Postgres 13 and later, with single-precision, half-precision, […]

Vector search used to mean bolting a separate database onto your stack. Two open-source PostgreSQL extensions have changed that: pgvector and pgvectorscale. Both are free, both run inside Postgres, and both have shipped meaningful upgrades in the past year — pgvector is at v0.8.6 with iterative index scans and half-precision vectors, while pgvectorscale 0.9.0 added […]

pgvector has become the default way to add vector search to PostgreSQL, and for good reason: you keep embeddings next to the relational data they describe, in the same database you already back up, monitor, and trust. But there is a widening gap between the demo and the production workload, and it usually shows up […]

Vector search is the engine room of most RAG applications in 2026, and pgvector has become the default choice for Houston teams who refuse to bolt a separate vector database onto their stack. But there is a dirty secret in the AI tooling world: most pgvector benchmarks — the ones you read and the ones […]

Vector search has moved from exotic to table stakes. pgvector is bundled into Amazon RDS and Aurora, Google Cloud SQL, Azure Database for PostgreSQL, and countless self-managed clusters — and the benchmark posts keep coming: 10,000 queries per second! 50x faster than brute force! The New Stack recently published a sharp critique of the genre […]