Retrieval Augmented Generation (RAG) Explained: Embedding, Sentence BERT, Vector Database (HNSW)

Get your 5$ coupon for Gradient: gradient.1stcollab.com/umarjamilai In this video we explore the entire Retrieval Augmented Generation pipeline. I will start by reviewing language models, their training and inference, and then explore the main ingredient of a RAG pipeline: embedding vectors. We will see what are embedding vectors, how they are computed, and how we can compute embedding vectors for sentences. We will also explore what is a vector database, while also exploring the popular HNSW (Hierarchical Navigable Small Worlds) algorithm used by vector databases to find embedding vectors given a query. Download the PDF slides: github.com/hkproj/retrieval-augmented-generation-notes Sentence BERT paper: arxiv.org/pdf/1908.10084.pdf Chapters 00:00 - Introduction 02:22 - Language Models 04:33 - Fine-Tuning 06:04 - Prompt Engineering (Few-Shot) 07:24 - Prompt Engineering (QA) 10:15 - RAG pipeline (introduction) 13:38 - Embedding Vectors 19:41 - Sentence Embedding 23:17 - Sentence BERT 28:10 - RAG pipeline (review) 29:50 - RAG with Gradient 31:38 - Vector Database 33:11 - K-NN (Naive) 35:16 - Hierarchical Navigable Small Worlds (Introduction) 35:54 - Six Degrees of Separation 39:35 - Navigable Small Worlds 43:08 - Skip-List 45:23 - Hierarchical Navigable Small Worlds 47:27 - RAG pipeline (review) 48:22 - Closing