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Local Retrieval Augmented Generation (RAG) from Scratch (step by step tutorial)
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https://whimsical.com/simple-local-rag-workflow-39kToR3yNf7E8kY4sS2tjV https://github.com/mrdbourke/simple-local-rag Timestamps: 0:00 - Intro/NVIDIA GTC 2:25 - Part 0: Resources and overview 8:33 - Part 1: What is RAG? Why RAG? Why locally? 12:26 - Why RAG? 19:31 - What can RAG be used for? 26:08 - Why run locally? 30:26 - Part 2: What we're going to build 40:40 - Original Retrieval Augmented Generation paper 46:04 - Part 3: Importing and processing a PDF document 48:29 - Code starts! Importing a PDF and making it readable 1:17:09 - Part 4: Preprocessing our text into chunks (text splitting) 1:28:27 - Chunking our sentences together 1:56:38 - Part 5: Embedding creation 1:58:15 - Incredible embeddings resource by Vicky Boykis 1:58:49 - Open-source embedding models 2:00:00 - Creating our embedding model 2:09:02 - Creating embeddings on CPU vs GPU 2:25:14 - Part 6: Retrieval (semantic search) 2:36:57 - Troubleshooting np.fromstring (live!) 2:41:41 - Creating a small semantic search pipeline 2:56:25 - Showcasing the speed of vector search on GPUs 3:20:02 - Part 7: Similarity measures between embedding vectors explained 3:35:58 - Part 8: Functionizing our semantic search pipeline (retrieval) 3:46:49 - Part 9: Getting a Large Langue Model (LLM) to run locally 3:47:51 - Which LLM should I use? 4:03:18 - Loading a LLM locally 4:30:48 - Part 10: Generating text with our local LLM 4:49:09 - Part 11: Augmenting our prompt with context items 5:16:04 - Part 12: Functionizing our text generation step (putting it all together) 5:33:57 - Summary and potential extensions 5:36:00 - Leave a comment if there's any other videos you'd like to see!
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