This repository contains a Jupyter Notebook demonstrating how to generate vector embeddings for both text and images using a multi-modal embedding model.
The first thing you'll want to do is create a virtual environment using your favorite technique. I tend to use venv, which comes with Python.
Once you've done that, install dependencies with:
pip install -r requirements.txt
You'll need to set an environment variable, MONGODB_URI, containing the connection string for your MongoDB cluster.
One more thing you'll need is an "images" directory, containing some images to index! I downloaded Kaggle's ImageNet 1000 (mini) dataset, which contains lots of images at around 4GB, but you can use a different dataset if you prefer. The notebook searches the "images" directory recursively, so you don't need to have everything at the top-level.
Then you can fire up the notebook with:
jupyter notebook "Image Search.ipynb"
At the end of the tutorial, you'll be able to search for images with snippets of text, like this:
