https://github.com/mongodb-developer/image-search-vector-demo
A Jupyter Notebook demonstrating how to use a multi-modal embedding model to build an image search engine.
https://github.com/mongodb-developer/image-search-vector-demo
ai jupyter-notebook python search vector
Last synced: 2 months ago
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A Jupyter Notebook demonstrating how to use a multi-modal embedding model to build an image search engine.
- Host: GitHub
- URL: https://github.com/mongodb-developer/image-search-vector-demo
- Owner: mongodb-developer
- License: apache-2.0
- Created: 2024-01-29T09:12:41.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-01-29T09:18:08.000Z (over 1 year ago)
- Last Synced: 2025-03-28T00:06:30.229Z (3 months ago)
- Topics: ai, jupyter-notebook, python, search, vector
- Language: Jupyter Notebook
- Homepage:
- Size: 3.61 MB
- Stars: 11
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# Image Search with MongoDB Atlas Vector Search
This repository contains a Jupyter Notebook demonstrating how to generate
vector embeddings for both text and images using a multi-modal embedding model.
## Getting Ready To Run The Notebook
The first thing you'll want to do is create a virtual environment using your favorite technique. I tend to use [venv](https://docs.python.org/3/library/venv.html), 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](https://www.kaggle.com/datasets/ifigotin/imagenetmini-1000), 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:
