We used it in an e-commerce application.
Apparently, one of the hardest things to do is to match a product name + description to a product taxonomy.
There are multiple taxonomies. Here's Google's for example: https://www.google.com/basepages/producttype/taxonomy.en-US....
Amazon has their own. Walmart has their own. Target has their own.
Given a list of tens of thousands of products, how can you automatically match the product to a merchant's taxonomy?
I started with a "clever" SQL query to do this, but it turns out that it's way easier to use vector DBs to do this.
1. Get the vector embedding for each taxonomy path and store this
2. Get the vector embedding for a given product using the name and a short description
3. Find the closest matching taxonomy path using vector similarity
It's astonishingly good at doing this and solved a big problem for us which was building a unified taxonomy from the various merchant taxonomies.
You can use the same technique to match products with high confidence across merchants by storing the second vector embedding. Now you have a way to determine that product A on Target.com is the same as product A' on Walmart.com is the same as product A'' on Amazon.com by comparing vector similarity.