Expert PostgreSQL tutorials, tips, and performance optimization guides. Learn database administration, query optimization, and best practices for PostgreSQL 18 and beyond.
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 […]
If you’ve been paying attention to the AI landscape in early 2026, you’ve almost certainly stumbled across the name OpenClaw. It blew past 280,000 GitHub stars in just weeks, Jensen Huang called it “the new Linux” at NVIDIA GTC 2026, and developers everywhere are asking the same question: where does PostgreSQL fit into all of […]
Remember when building an AI app meant stitching together five different databases? A relational database for your core data, Pinecone for vectors, Elasticsearch for search, Redis for caching, and maybe InfluxDB if you had time-series needs. Each one with its own query language, backup strategy, and 3 a.m. pager duty incident. That era is over. […]
Retrieval-Augmented Generation (RAG) has become one of the most practical applications of AI in production systems, and PostgreSQL’s pgvector extension has emerged as a robust solution for implementing vector search. However, getting optimal results requires more than just installing pgvector and throwing embeddings at it. Let’s explore proven best practices and real-world examples to help […]
PostgreSQL has quietly crossed an important line in the last year: AI with Postgres is no longer a science project, it is genuinely competitive with specialized vector databases at production scale. From “possible” to “competitive” For a long time, the story around AI and Postgres sounded like “you can do it if you really want […]
