docs/content/v2024.2/additional-features/pg-extensions/extension-pgvector.md
The pgvector PostgreSQL extension allows you to store and query vectors, for use in performing similarity searches.
Note that YugabyteDB support for pgvector does not currently include indexing.
To enable the extension:
CREATE EXTENSION vector;
Create a vector column with 3 dimensions:
CREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3));
Insert vectors:
INSERT INTO items (embedding) VALUES ('[1,2,3]'), ('[4,5,6]');
Get the nearest neighbors by L2 distance:
SELECT * FROM items ORDER BY embedding <-> '[3,1,2]' LIMIT 5;
The extension also supports inner product (<#>) and cosine distance (<=>).
Note: <#> returns the negative inner product because PostgreSQL only supports ASC order index scans on operators.
Create a new table with a vector column:
CREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3));
Or add a vector column to an existing table:
ALTER TABLE items ADD COLUMN embedding vector(3);
Insert vectors:
INSERT INTO items (embedding) VALUES ('[1,2,3]'), ('[4,5,6]');
Upsert vectors:
INSERT INTO items (id, embedding) VALUES (1, '[1,2,3]'), (2, '[4,5,6]')
ON CONFLICT (id) DO UPDATE SET embedding = EXCLUDED.embedding;
Update vectors:
UPDATE items SET embedding = '[1,2,3]' WHERE id = 1;
Delete vectors:
DELETE FROM items WHERE id = 1;
Get the nearest neighbors to a vector:
SELECT * FROM items ORDER BY embedding <-> '[3,1,2]' LIMIT 5;
Get the nearest neighbors to a row:
SELECT * FROM items WHERE id != 1 ORDER BY embedding <-> (SELECT embedding FROM items WHERE id = 1) LIMIT 5;
Get rows within a certain distance:
SELECT * FROM items WHERE embedding <-> '[3,1,2]' < 5;
Get the distance:
SELECT embedding <-> '[3,1,2]' AS distance FROM items;
For inner product, multiply by -1 (<#> returns the negative inner product)
SELECT (embedding <#> '[3,1,2]') * -1 AS inner_product FROM items;
For cosine similarity, use 1 - cosine distance:
SELECT 1 - (embedding <=> '[3,1,2]') AS cosine_similarity FROM items;
Average vectors:
SELECT AVG(embedding) FROM items;
Create a table with a vector column and a category column:
CREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3), category_id int);
Insert multiple vectors belonging to the same category:
INSERT INTO items (embedding, category_id) VALUES ('[1,2,3]', 1), ('[4,5,6]', 2), ('[3,4,5]', 1), ('[2,3,4]', 2);
Average groups of vectors belonging to the same category:
SELECT category_id, AVG(embedding) FROM items GROUP BY category_id;