Faster Vector Search With Centroid Clustering

Vector search is a powerful tool for semantic natural language search, but it can be computationally intensive and slow to run on some platforms. In this demo I will show how k-means clustering similar to that used in FAISS to speed up your own vector search implementation regardless of platform. This works by pre-clustering your vectors into groups with a central vector point and searching these groups based on the central vector match score with the user query.