Vector Analysis
How to perform High-Performance Search using FAISS
A Beginner’s Guide to FAISS, use-cases, Mathematical foundations & implementation
FAISS (Facebook AI Similarity Search) is an open-source library developed by Facebook AI Research (FAIR) for high-dimensional data similarity search and clustering. It’s very beneficial for large-scale machine learning tasks including nearest neighbour search, clustering, and approximate nearest neighbour search. In this blog post, we will discuss FAISS, including its benefits, use cases, and how to implement it using Python.
What is similarity search?
Real-world objects and concepts are frequently represented in machine learning as a set of continuous numbers, sometimes known as vector embeddings. This approach converts the similarity of items as interpreted by us into a vector space
This means that when we represent images or text as vector embeddings, the semantic similarity of the images or text is expressed by how near their vectors are in the vector space. As a result, we want to look at the distance between the objects’ vectors to find similar objects
A similarity search for vectors is a technique used to locate vectors in a high-dimensional space…
