Learn how to build your own Google with RAG (Full Course)

Description

Master Search and RAG with this complete course by Isaac Flath. From keyword search to multimodal embeddings, learn how to build modern AI search systems hands-on. Learn Retrieval Augmented Generation - https://www.boot.dev/courses/learn-retrieval-augmented-generation Code "isaac" for 25% off. Go check out Isaac's channel! (https://www.youtube.com/@isaac-flath) CHAPTERS: 00:00 - Ch1 L1 - Retrieval Augmented Generation 01:29 - Ch1 L2 - What is search 05:41 - Ch1 L3 - Project Overview 07:52 - Ch1 L4 - Keyword Search 15:52 - Ch1 L5 - Text Processing 18:28 - Ch1 L6 - Punctuation 20:59 - Ch1 L7 - Tokenization 25:52 - Ch1 L8 - Stop words 33:35 - Ch1 L9 - Stemming 37:26 - Ch2 L1 - Inverted Index 45:52 - Ch2 L2 - Use the index 50:38 - Ch2 L3 - Boolean Search 52:05 - Ch2 L4 - Term Frequency 01:02:46 - Ch2 L5 - Inverse Document Frequency 01:08:06 - Ch2 L6 - TF-IDF 01:11:21 - Ch3 L1 - Keyword Search 01:18:58 - Ch3 L2 - Term Frequency Saturation 01:25:03 - Ch3 L3 - Document Length Normalization 01:36:00 - Ch3 L4 - BM25 Search 01:47:03 - Ch4 L1 - Semantic Search 01:50:23 - Ch4 L2 - Embeddings 01:52:04 - Ch4 L3 - Embedding Models 01:56:41 - Ch4 L4 - Model Selection 01:59:38 - Ch4 L5 - Vector Operations 02:02:31 - Ch4 L6 - Dimensions 02:04:22 - Ch4 L7 - Dot Product Similarity 02:06:18 - Ch4 L8 - Why Cosine Similarity? 02:10:54 - Ch4 L9 - Why Cosine Similarity 02:16:46 - Ch4 L11 - Document Embeddings 02:26:21 - Ch4 L12 - Query Embeddings 02:29:51 - Ch4 L13 - Same Model 02:32:17 - Ch4 L14 - Implementing Semantic Search 02:40:18 - Ch4 L15 - Locally-Sensitive Hashing 02:42:00 - Ch4 L16 - Vector Databases 02:44:14 - Ch5 L1 - Chunking 02:51:17 - Ch5 L2 - Chunk Overlap 02:57:17 - Ch5 Challenge - Semantic search basics 02:58:47 - Ch5 L3 - Semantic Chunking 03:06:03 - Ch5 L4 - Chunked Semantic Embeddings 03:19:49 - Ch5 L5 - Chunked Semantic Search 03:34:07 - Ch5 L6 - Chunked Edge Cases 03:37:47 - Ch5 L7 - ColBERT 03:41:03 - Ch5 L8 - Late Chunking 03:43:19 - Ch6 L1 - Keyword vs Semantic 03:44:50 - Ch6 L2 - Hybrid Search 03:48:53 - Ch6 L3 - Score Normalization 03:55:56 - Ch6 L4 - Weighted Combination 04:14:01 - Ch6 L5 - Reciprocal Rank Fusion 04:25:50 - Ch7 L1 - Large Language Models 04:27:29 - Ch7 L2 - Gemini API Setup 04:32:42 - Ch7 L3 - Spell Correction 04:40:59 - Ch7 L4 - Query Rewriting 04:43:35 - Ch7 L5 - Query Expansion 04:47:22 - Ch8 L1 - Re-Ranking 04:50:33 - Ch8 L2 - LLMs for Re-Ranking 05:05:45 - Ch8 L3 - LLM Batch Re-Ranking 05:18:41 - Ch8 L4 - Cross-Encoder Re-Ranking 05:25:38 - Ch9 L1 - Manual Evaluation 05:29:04 - Ch9 L2 - Golden Dataset 05:32:50 - Ch9 L3 - Precision Metrics 05:41:48 - Ch9 L4 - Recall Metrics 05:52:28 - Ch9 L5 - F1 Score 05:58:16 - Ch9 L6 - Error Analysis 06:12:46 - Ch9 L7 - LLM Evaluation 06:24:41 - Ch9 Challenge - Designing Chunking Strategy (Interview) 06:29:05 - Ch9 Challenge - Query -- Document Embeddings 06:30:14 - Ch10 L1 - Augmented Generation 06:40:20 - Ch10 L2 - LLM Summarization 06:47:34 - Ch10 L3 - Conflict Resolution in Summaries 06:49:33 - Ch10 L4 - Adding Citations 06:56:20 - Ch10 L5 - Question Answering 06:59:22 - Ch11 L1 - Recursive Rag 07:01:19 - Ch11 L2 - Agentic Search 07:03:12 - Ch12 L1 - Multimodal Search 07:11:19 - Ch12 L2 - Multimodal Embeddings 07:14:17 - Ch12 L3 - Image Embeddings 07:20:45 - Ch12 L4 - Multimodal Search Implementation Like & subscribe for the algo if you enjoyed the video!