RAG Explained: Understanding Embeddings, Similarity, and Retrieval

Retrieval-Augmented Generation (RAG) works by first transforming text into embeddings, which are high-dimensional vector representations that capture semantic meaning. These embeddings allow for mathematical comparisons between a user's query and chunks of a knowledge base. The similarity between the query vector and the document vectors is typically measured using cosine similarity, which calculates the angle between them to determine contextual relevance. The system then retrieves the top-k most similar document chunks, which are those with the highest cosine similarity scores, to provide context for generating an answer.