Visual and Semantic Similarity with Redis

This demo goes along with the Announcement of a New Redis Vector Similarity Search

You will experiment with two key applications of Vector Similarity Search application using a realistic dataset:

• Semantic Search: Given a sentence check products with semantically similar text in the product keywords
• Visual Search: Given a query image, find the Top K most "visually" similar in the catalogue

About the Amazon Product dataset

The CSV product data used in this demo was derived from the "Amazon Berkeley Objects Dataset"

Each row in the CSV file correspond to a product in the original dataset.

Before you start

• Install Git LFS
  • Make sure you initialize LFS by runing

  git lfs install

• Docker
• Docker Compose

Clone the Repo

git clone https://github.com/RedisAI/vecsim-demo.git

Fire Up the Docker containers

Use docker compose to start up 2 containers:

• vesim: A redis container with Vector Similarity Search (VSS) on port 6379
• jupyter: A python notebook server on port 8888 pre-loaded with 4 notebooks
  • 2 notebooks illustrating how to perform Visual Similarity with Redis VSS
  • 2 notebooks illustrating how to perform semantic Similarity with Redis VSS

cd vecsim-demo
docker compose up

NOTE: The first time you run the above command, it will take 5-10 minutes (depending on your network) The jupyter container downloads a 3.25GB tar file with product images from the "Amazon Berkeley Objects Dataset"

Launch the Jupyter Notebooks

Monitor the logs and look out for the link to launch jupyter on your local machine Or run the following:

jupyter notebook

Open a local browser to this link

Step 1: Semantic Similarity - Part I

Open this notebook http://127.0.0.1:8888/notebooks/SemanticSearch1k.ipynb

Run All Cells and check the outputs

You will generate embeddings for 1,000 products and perform semantic similarity using two indexing methods(HNSW and brute-force)

Step 2: Semantic Similarity - Part II

Open this notebook http://127.0.0.1:8888/notebooks/SemanticSearch100k.ipynb

Run All Cells and check the outputs

You will load ~100k previously-generated embeddings for the first 100,000 products in the dataset. You'll perform semantic similarity on a larger dataset

Step 3: Visual Similarity - Part I

Open this notebook http://127.0.0.1:8888/notebooks/VisualSearch1k.ipynb

Run All Cells and check the outputs

You will generate embeddings for 1,000 product images and perform visual similarity using two indexing methods

Step 4: Visual Similarity - Part II

Open this notebook http://127.0.0.1:8888/notebooks/VisualSearch100k.ipynb

You'll perform visual similarity on a larger dataset using two indexing methods (HNSW and brute-force)

Stop the Docker containers

docker compose down

About the Amazon Product data

The dataset used in this demo was derived from the "Amazon Berkeley Objects Dataset"

In particular, each long text field in the product_data.csv was extracted from the original JSON encoded object representing each product.

Thanks to Amazon.com for sharing the original dataset. This includes all product data, images and 3D models under the Creative Commons Attribution-NonCommercial 4.0 International Public License (CC BY-NC 4.0)

Credit to the creators of the dataset: Matthieu Guillaumin Amazon.com Thomas Dideriksen Amazon.com Kenan Deng Amazon.com Himanshu Arora Amazon.com Arnab Dhua Amazon.com Xi (Brian) Zhang Amazon.com Tomas Yago-Vicente Amazon.com Jasmine Collins UC Berkeley Shubham Goel UC Berkeley Jitendra Malik UC Berkeley