Ivee for PostgreSQL and AI

pgvector

In the rapidly evolving world of AI, the ability to efficiently store, manage, and query vector embeddings has become paramount. pgvector extension steps in to address this need, seamlessly extending the capabilities of PostgreSQL to handle these high-dimensional mathematical representations that underpin many modern AI applications.

From powering semantic search engines that understand the meaning behind queries to enabling recommendation systems that anticipate user preferences, pgvector’s applications span a wide spectrum.

With Ivee for PostgreSQL you can easily enable pgvector extension and unlock a new level of AI-driven functionality within your database.

• Connect to your database:

  psql -U your_username -h ivee_pg_host -p ivee_pg_port
  

  You define the username at the cluster creation time. The host and the port can be obtained from Connectivity tab in Ivee portal.

• Enable pgvector extension:

  CREATE EXTENSION vector;
  

Your database is ready for vector data.

Verify and test

You can quickly validate if pgvector works as expected by running the following commands:

• Create a simple table:

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

• Insert some sample embeddings:

  INSERT INTO items (embedding) VALUES
      ('[1.2, 3.4, 5.6]'),
      ('[0.1, 0.9, 2.3]'),
      ('[4.5, 6.7, 8.9]');
  

• Find the nearest neighbor to a query embedding:

  SELECT * FROM items
  ORDER BY embedding <-> '[1.0, 3.0, 6.0]'
  LIMIT 1;
  

  This query uses the <-> operator to calculate the Euclidean distance between vectors.

• Clean up - drop the table:

  DROP TABLE items;
  

Feedback and help

Percona Ivee is currently in Beta. We are craving for feedback and looking forward to your questions at the email below!

ivee@percona.com