Distributed DB
PostgreSQL 17 and pgvector in Practice: Hundred-Million-Scale Vector Search and HNSW Index Tuning
As RAG architectures spread, the operational cost of running a dedicated vector database stays stubbornly high. This article covers using the pgvector extension in PostgreSQL 17, with HNSW (Hierarchical Navigable Small World) and IVFFlat indexes, to build an enterprise knowledge retrieval engine holding 10 million vectors on a single node with 10ms response times.
#HNSW
#pgvector
#PostgreSQL 17
pgvector_hybrid_search.sqlsql
-- PostgreSQL 17: create an HNSW vector index and run a fast hybrid search
CREATE EXTENSION IF NOT EXISTS vector;
CREATE TABLE tech_knowledge_base (
id BIGSERIAL PRIMARY KEY,
category VARCHAR(64),
title TEXT,
content TEXT,
embedding vector(1536) -- OpenAI text-embedding-3 dimensionality
);
-- Build the HNSW index (m=16 neighbors, ef_construction=64 build depth)
CREATE INDEX O
Alexander Zhao
2025-02-04