Vector Databases — Complete Guide

Vector Databases — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of PostgreSQL Tutorial on Toolliyo Academy.

8 min read Updated 8/22/2026

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PostgreSQL Tutorial · Lesson 57 of 100

Vector Databases

SQL ✓ → Advanced

Advanced · 2 — Production · ~10 min · PostgreSQL — JSONB & Modern Features

What is this?

Vector databases optimize nearest-neighbor search on embeddings — in PostgreSQL via pgvector indexes (IVFFlat, HNSW) and distance operators <->, <#>, <=>.

Why should you care?

PostgresVerse “customers also bought” uses cosine distance on product embeddings instead of slow Python loops.

See it live — copy this example

Run in pgAdmin or psql.

CREATE INDEX ON product_embeddings
USING hnsw (embedding vector_cosine_ops);

SELECT p.name, pe.embedding <=> '[0.1,0.2,0.9]' AS distance
FROM product_embeddings pe
JOIN products p ON p.product_id = pe.product_id
ORDER BY pe.embedding <=> '[0.1,0.2,0.9]'
LIMIT 5;

What happened?

HNSW index speeds approximate nearest neighbor.
<=> is cosine distance operator.
Query returns five most similar products to query vector.

Practice next

Load embeddings from AI Extensions lesson.
CREATE HNSW or IVFFlat index after ANALYZE.
Run ORDER BY distance LIMIT 5.
Filter: WHERE category='electronics' ORDER BY distance LIMIT 10.
Compare sequential scan vs index timing on 100k vectors.

Remember

pgvector brings ANN search into Postgres. HNSW often best recall/latency on moderate data. Normalize vectors if using cosine on unnormalized embeddings.