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| Name | Retrieval Augmented Generation (RAG) Explained: Embedding, Sentence BERT, Vector Database (HNSW) |
| Video Uploader | Video From Umar Jamil |
| Upload Date | This Video Uploaded At 27-11-2023 07:59:38 |
| Video Discription | Get your 5$ coupon for Gradient: https://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: 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 |
| Category | Science & Technology |
| Tags | 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 |
