README.md

An app for searching for images matching a text, using CLIP, PostgreSQL® and pgvector

A Python web web app that searches for images matching a given text

First, create a virtual environment to keep package installation local to this directory

python3 -m venv venv

Enable it - this shows doing so for a normal Unix shell, there are other scripts for (for instance) the fish shell

source venv/bin/activate

Install the Python packages we need

python3 -m pip install -r requirements.txt

Note Sometimes we've seen the Python clip.load function fail to download the CLIP model, presumably due to the source server being busy. The code here will use a local copy of the model if it's available. To make that local copy:

mkdir models
curl https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt --output models/ViT-B-32.pt

Create your PostgreSQL® database. An Aiven for PostgreSQL service will do very well - see the Create a service section in the Aiven documentation.

Copy the template environment file

cp .env_example .env

Then edit the .env file to insert the credentials needed to connect to the database.

Note If you're using an Aiven for PostgreSQL service, then you want the Service URI value from the service Overview in the Aiven console. The result should look something like:

PG_SERVICE_URI=postgres://<user>:<password>@<host>:<port>/defaultdb?sslmode=require

Enable pgvector and set up the table we need in the database

./create_table.py

Calculate the embeddings for the pictures in the photos directory, and upload them to the database

./process_images.py

You can run find_images.py to check that everything is working - it looks for images matching the text man jumping and reports their filenames

./find_images.py

Run the webapp locally using fastapi

fastapi dev app.py

Go to http://127.0.0.1:8000 in a web browser, and request a search.

Possible ideas include:

• cat
• man jumping
• outer space

The photos

The images in the photos directory are the same as those used in Workshop: Searching for images with vector search - OpenSearch and CLIP model.

They came from Unsplash and have been reduced in size to make them fit within GitHub filesize limits for a repository.

Inspirations

• The Workshop: Searching for images with vector search - OpenSearch and CLIP model which does (essentially) the same thing, but using OpenSearch and Jupyter notebooks.

• Building a movie recommendation system with Tensorflow and PGVector which searches text, and produces a web app using JavaScript

For help understanding how to use HTMX

• Using HTMX with FastAPI
• and for help understanding how I wanted to use forms, Updating Other Content from the HTMX documentation (I went for option 1, as suggested).