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:
- 5432:5432
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 5432
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 initialization purposes.