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