Vector databases explained

Short summary

Vector databases power AI search and RAG by storing embeddings—numeric representations of meaning—and retrieving nearby vectors via approximate nearest neighbor algorithms. For most products up to millions of vectors, PostgreSQL's pgvector extension is the pragmatic choice, avoiding a separate system while enabling joins between vector and relational data. Graduate to a dedicated vector store only at massive scale or high query throughput, but focus first on retrieval quality: domain-matched embeddings, hybrid search, filtering, and re-ranking matter more than your engine choice.

• •Embeddings convert text meaning into vectors; similarity search finds nearby vectors using ANN algorithms for speed
• •pgvector on PostgreSQL is the right starting point for most products—single system, joins with relational data, no extra infrastructure
• •Retrieval quality depends more on embedding model selection, hybrid search, filtering, and re-ranking than on database choice

Generated with AI, which can make mistakes.