PGVector Tutorial 2026: Add AI Search to PostgreSQL in 30 Minutes

You already use PostgreSQL - adding vector search keeps your stack simple. PGVector is the most popular way (22,400 stars).

💡 What You Will Learn

You already use PostgreSQL - adding vector search keeps your stack simple. PGVector is the most popular way (22,400 stars).

📜 Table of Contents

• PGVector Tutorial 2026: Add AI Search to PostgreSQL in 30 Minutes
• Why PGVector Instead of a New Database
• Step 1: Install the Extension
• Step 2: Create a Table
• Step 3: Insert Embeddings (Python)
• Step 4: Search
• Step 5: Add an Index (required at scale)
• FAQ

PGVector Tutorial 2026: Add AI Search to PostgreSQL in 30 Minutes

PGVector is the PostgreSQL extension that adds vector storage and similarity search to your existing database. At 22,400 GitHub stars, it is the most popular way to add semantic search without introducing a separate vector database. If you already run Postgres, this tutorial gets you a working RAG search in half an hour.

Why PGVector Instead of a New Database

Step 1: Install the Extension

CREATE EXTENSION IF NOT EXISTS vector;

On Ubuntu: apt install postgresql-16-pgvector. On Mac: brew install pgvector.

Step 2: Create a Table

CREATE TABLE documents (
    id SERIAL PRIMARY KEY,
    content TEXT,
    embedding vector(768)
);

The 768 dimension must match your embedding model (bge-m3 uses 1024; nomic-embed-text uses 768).

Step 3: Insert Embeddings (Python)

import psycopg2
from openai import OpenAI

client = OpenAI(base_url="http://localhost:11434/v1", api_key="ollama")
vec = client.embeddings.create(model="nomic-embed-text", input="your text").data[0].embedding

conn = psycopg2.connect("dbname=app")
conn.execute(
    "INSERT INTO documents (content, embedding) VALUES (%s, %s)",
    ("your text", vec)
)

Step 4: Search

SELECT content, 1 - (embedding <=> query_embedding) AS similarity
FROM documents
ORDER BY embedding <=> query_embedding
LIMIT 5;

The <=> operator is cosine distance. Lower = more similar.

Step 5: Add an Index (required at scale)

CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops);

HNSW indexes make search milliseconds instead of seconds once you pass ~10K rows.

FAQ

PGVector vs dedicated vector DBs? PGVector wins on simplicity and consistency for most apps under ~10M vectors. Chroma (28,900 stars), Qdrant (33,700), and Milvus (45,400) win at massive scale or when you want a purpose-built store.

Does it work with LangChain? Yes - langchain-postgres has a PGVector vectorstore class.

HNSW or IVFFlat index? HNSW - better accuracy and no tuning needed. IVFFlat is legacy.

❓ FAQ

PGVector vs dedicated vector DBs?

PGVector wins on simplicity and consistency for most apps under ~10M vectors. Chroma (28,900 stars), Qdrant (33,700), and Milvus (45,400) win at massive scale or when you want a purpose-built store.

Does it work with LangChain?

Yes - langchain-postgres has a PGVector vectorstore class.

HNSW or IVFFlat index?

HNSW - better accuracy and no tuning needed. IVFFlat is legacy.