Supercharged Semantic Similarity Search in Production

Blazing Fast, Highly Scalable Text-to-Image Search with CLIP embeddings and Milvus

Supercharge! Photo by Josh Calabrese on Unsplash

Introduction

I introduced three simple methods to convert images into embeddings for similarity search applications using state-of-the-art neural networks in my previous post. In this post, let's discuss how we can use those embeddings together with Milvus, one of the most popular open-source vector search databases, to create a production scale Text-to-Image search application. Specifically, in this post, we'll cover

1. Considerations for production-scale vector similarity search
2. Setup the environment and download some data
3. Implementing a text-to-image similarity search application using Milvus

For this post, we'll be using the H&M Personalized Fashion Recommendations competitions dataset from Kaggle available here to build a highly scalable text-to-image e-commerce product search service.

Considerations for production-scale vector similarity search

In my previous post, we found similar images by calculating the cosine similarity between a query image's vector and all the other image vectors we…