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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.

Download the PDF slides: https://github.com/hkproj/retrieval-augmented-generation-notes
Sentence BERT paper: https://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

Видео Retrieval Augmented Generation (RAG) Explained: Embedding, Sentence BERT, Vector Database (HNSW) канала Umar Jamil
deep learning pytorch ai ml machine learning paper review llm large language model question answering fine tuning retrieval augmented generation sentence bert sentence transformers embedding word embedding sentence embedding hierarchical navigable small worlds navigable small worlds vector db vector database knn k nearest neighbors k-nn
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27 ноября 2023 г. 12:59:38
00:49:24
Umar Jamil
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