Create a empty folder in your home directory.

$ cd ~/
$ mkdir pgvector

Now that you have added a folder to your home directory for persisting your database. Now we can run a docker command to start a vector store for our RAG application.

$ docker run -d --name pgvector -e POSTGRES_PASSWORD=password -v ${HOME}/pgvector/:/var/lib/postgresql/data -p 5432:5432 pgvector/pgvector:pg16

Now that we have started a database server on our computer with Docker, we can log into the running container to create a database that we can use to store our vectors.

$ docker exec -it pgvector psql -U postgres
$ create database jaxnodevector;
# DATABASE CREATED
$ CREATE EXTENSION IF NOT EXISTS vector;
# EXTENSION vector CREATED
\q

The first line creates an interactive terminal on our container, and uses the psql command line application for us to interact with the postgres system database. On the second line we create a database called jaxnodevector.

On the last line we use the \q command to exit out of the psql program.

Now that we have a postgres database with the PGVector extension, we can create a table to store our vectors. I have created a script you can use to do this in this example. Run the following script with Node.js or the JavaScript engine of your choice.

$ npm run pgvector:setup

This script will connect to your

Now we can install Ollama locally. Go to Ollama.ai to download Ollama or install using docker.

$ docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
$ docker exec -it ollama ollama run llama2