A Beginner’s Guide to Similarity Search & Vector Indexing (Part One)

Image created by author

Part 1, Part 2, and Part 3 of this series.

Similarity search in the context of Retrieval Augmented Generation (RAG) involves finding and retrieving relevant pieces of information or data points that are similar to a given query vector. RAG is an approach in generative AI that combines retrieval-based methods with generative models to enhance the quality and relevance of generated content. Vector indexing methods are techniques used to efficiently store, organize, and retrieve high-dimensional data points or vectors in databases and data structures. The choice of vector indexing method depends on various factors, including the dimensionality of the data, the trade-off between precision and recall, the size of the dataset, and the computational resources available. Different methods excel in different scenarios, so selecting the appropriate indexing method is crucial to achieving efficient and accurate search in high-dimensional vector spaces.

RAG Process (image created by author)

Similarity metrics: In RAG (Retrieval Augmented Generation), a similarity metric is a mathematical measurement used to evaluate how similar or relevant a query vector is to vectors in a dataset…