Using the RedisVL library, I'm building out a simple vector search demo. I'll be taking a dataset of anime from Kaggle and vectorizing both the main poster image and the description. I'll then be able to search over the dataset using text and see either the closest image or the closest description to the text I entered. As a nerd who loves anime, this is a fun project for me to work on.
If you want to run the search over anime posters, you'll need to first get the anime dataset for yourself from Kaggle - it requires is an email to download, but it's free.
You'll also need a Redis connection, with the two easiest options presented below.
For me and my environment, running this from scratch looks like:
git clone https://github.com/sav-norem/redis_vectorsearch.gitCD redis_vectorsearchpython3 -m venv .source bin/activatepoetry install- Put
anime-dataset-2023.csv(the file you downloaded from Kaggle) in the same folder as thepoetry.lockfile python3 src/redisvl_demo/redisvl_demo.py
This will bring up a link to the local web app where you can now search using text over the top ~1,000 anime posters.
This project has a DataLoader and a Demo. The DataLoader will take optional arguments for both an initial file and a parsed file path. If you're doing this with the same data I did, you won't need to change anything. However if you want to swap out data, change file paths, etc. both of these inputs will need to change. You can do so in redisvl_demo.py on the initialization of DataLoader. Both Demo and DataLoader will take an optional argument for an index_name. Again, if you're not trying to change up the data or do your own testing, you can leave this alone. They both also take a Redis connection, meaning redisvl_demo.py is where you'll need to change that if you want to use anything other than r = redis.Redis(host='localhost', port=6379, db=0).
To connect to Redis you have options. The route I took was to run Redis from docker with docker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:latest, so if you're also running Redis locally with the default port you shouldn't have to touch the connection string. If you want to connect to a Cloud database you can replace the r = redis.Redis(host='localhost', port=6379, db=0) with your connection details. Those can be found in the Redis Cloud console by clicking Connect on your database, then navigating to Redis Client:
This demo takes a bit to load and has a print statement mostly for entertainment / progress purpose. If you'd rather stare at an empty terminal while data gets loaded, you're more than welcome to take out the print statement. Regardless, parsing this data, getting the images, vectorizing them and loading them, takes a bit of time.
The vector_extend file overwrites the HuggingFace embed function from the RedisVL library to allow for images. I'm currently using two different models, one for the images and one for the synopsis. While sentence-transformers/clip-ViT-L-14 is multi-modal and can be used for text, the limit for tokens was too low to vectorize the entire synopsis. I'll definitely be exploring other models for these purposes and seeing how they impact the search results.
Features I'm planning to add:
Refactor to separate the data loader and the demo runnerSearch over synopsis as well as imageA way to see more than just the top result from the search- Filter buttons for different genres
- Test out other models for the vectorization
If you want to check out the extra simple version, the first iteration, vl_demo under old_versions, uses images of strawberries since I'd been writing about why LLMs are bad at spelling. You'll still need to do the poetry install followed by python3 old_versions/vl_demo.py and you'll see a simple search interface that allows you to search by text and see which strawberry image is closest to the text you entered. This demo will load a lot faster, but doesn't display images.
I'm blogging about this project as I go, so if you have any questions or want to know more, feel free to reach out to me on dev.to or LinkedIn.