Implementing the pgvector extension for a PostgreSQL database

If you’re looking to implement vectorized search using PostgreSQL and Python, you’re in luck! In this article, we’ll explore how you can set up a PostgreSQL database with the pgvector extension and utilize it to perform vectorized searches.

To get started, we’ll be using an existing demo repository called “pgvector-demo”, which showcases the implementation of vectorized search. You can find the repository on GitHub and clone it to your local machine (https://github.com/johannesocean/pgvector-demo).

Firstly, we need to create a docker-compose.yml file with the necessary services. Create a file named docker-compose.yml and add the following contents:

services:
  db:
    hostname: db
    image: ankane/pgvector
    ports:
     - 5555:5432
    restart: always
    environment:
      - POSTGRES_DB=vectordb
      - POSTGRES_USER=testuser
      - POSTGRES_PASSWORD=testpwd
      - POSTGRES_HOST_AUTH_METHOD=trust
    volumes:
     - ./init.sql:/docker-entrypoint-initdb.d/init.sql

In this docker-compose.yml file, we define a service called db which is based on the ankane/pgvector Docker image. The service exposes port 5555 on the host machine (which is mapped to port 5432 in the Docker container) to interact with the database and sets up environment variables for the database name, user, password, and authentication method. Additionally, we mount an init.sql file to the /docker-entrypoint-initdb.d directory inside the container for…