Open-source vector similarity search for Postgres. Store your vectors with the rest of your data. It is implemented as a Postgres extension that needs to be installed and enabled. Follow the installation instructions for pgvector here.
Alternatively, You can use docker compose to stand up a Postgres instance already enabled with pgvector.
docker run --name pgvector -d -e POSTGRES_PASSWORD=postgres ankane/pgvector
docker exec -it pgvector bash
$ psql -U postgresSentenceTransformers provides models that embed images and text into the same vector space. This allows us to find similar images and implement image search. SentenceTransformers provide a wrapper for the OpenAI CLIP Model, which was trained on various (image, text)-pairs. You can read more here.
Psycopg is a popular python driver for Postgres. We will use it directly with pgvector to insert and invoke similarity searches of images.
Create a Python environment and install the Python module requirements.
python -m venv .venv
pip install --upgrade --quiet -r requirements.txt To run the demo, execute the command below. The -s will seed the Postgres database with the images in the images directory. The -s can be omitted. You must reseed the database if you add images or change file names.
$ python example-psycopg.py -s
seeding the database
Enter image query: show me a picture of NYCThe application will pop up a picture closely matching your query and the distance value.
Similar to LangChain, Vecs is a Python client library for managing and querying vector stores in PostgreSQL, leveraging the capabilities of the pgvector extension.
\dt vecs.image_vectors