How do I enable the pgvector extension in my Neon database?

Connect to your database and run CREATE EXTENSION IF NOT EXISTS vector;. That's the whole install step. pgvector is available on every Neon plan with no add-on or paid tier required. You can run the statement from the Neon SQL Editor, psql, or any Postgres client. See The pgvector extension for distance operators, index types, and supported vector types.

Enable the extension

CREATE EXTENSION IF NOT EXISTS vector;

Run it once per database. The extension is installed per database, not per project, so if you have multiple databases on a branch you need to enable it in each one.

To confirm it's installed and check the version:

SELECT extname, extversion
FROM pg_extension
WHERE extname = 'vector';

To install the prior supported version (one back from latest):

CREATE EXTENSION vector VERSION '0.7.4';

Check the Postgres extensions page for the current latest supported version on Neon.

Try it with embeddings

Create a table with a vector column, insert two rows, and run a similarity query:

CREATE TABLE items (
  id BIGSERIAL PRIMARY KEY,
  embedding vector(3)
);

INSERT INTO items (embedding) VALUES ('[1,2,3]'), ('[4,5,6]');

SELECT id, embedding <-> '[3,1,2]' AS distance
FROM items
ORDER BY embedding <-> '[3,1,2]'
LIMIT 5;

<-> is L2 distance. pgvector also supports <#> (negative inner product), <=> (cosine distance), and <+> (L1 distance).

For production-sized datasets, add an approximate index. HNSW is a good default; it doesn't need a training step and has the better speed-recall tradeoff:

CREATE INDEX ON items USING hnsw (embedding vector_cosine_ops);

Match the operator class (vector_cosine_ops, vector_l2_ops, vector_ip_ops, and so on) to the distance function you query with.

Tune pgvector for production

Walk through HNSW vs IVFFlat, vector types (halfvec, bit, sparsevec), and query tuning for similarity search.

Read the pgvector docs