Contextual Retrieval with Any LLM: A Step-by-Step Guide

Video Description

In this video, I show you how to implement contextual retrieval for any LLM using a strategy from Anthropic. We'll go through the entire process, from chunk creation to embedding storage, using OpenAI models and tools like LangChain and BM25. Perfect for enhancing your document retrieval system's accuracy. LINKS: Colab: https://tinyurl.com/2p9wwypy Anthropic Blogpost: https://www.anthropic.com/news/contextual-retrieval Late chunking: https://jina.ai/news/late-chunking-in-long-context-embedding-models/ 💻 RAG Beyond Basics Course: https://prompt-s-site.thinkific.com/courses/rag Let's Connect: 🦾 Discord: https://discord.com/invite/t4eYQRUcXB ☕ Buy me a Coffee: https://ko-fi.com/promptengineering |🔴 Patreon: https://www.patreon.com/PromptEngineering 💼Consulting: https://calendly.com/engineerprompt/consulting-call 📧 Business Contact: [email protected] Become Member: http://tinyurl.com/y5h28s6h 💻 Pre-configured localGPT VM: https://bit.ly/localGPT (use Code: PromptEngineering for 50% off). Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0 00:00 Introduction to Contextual Retrieval 00:35 Recap of Standard RAG System 02:08 Implementing Contextual Retrieval 04:00 Setting Up the Environment 05:37 Creating Contextualized Chunks 06:56 Generating Contextualized Prompts 08:43 Building Vector Stores and Search Indices 09:55 Example Queries and Results 13:29 Advanced Techniques and Conclusion All Interesting Videos: Everything LangChain: https://www.youtube.com/playlist?list=PLVEEucA9MYhOu89CX8H3MBZqayTbcCTMr Everything LLM: https://youtube.com/playlist?list=PLVEEucA9MYhNF5-zeb4Iw2Nl1OKTH-Txw Everything Midjourney: https://youtube.com/playlist?list=PLVEEucA9MYhMdrdHZtFeEebl20LPkaSmw AI Image Generation: https://youtube.com/playlist?list=PLVEEucA9MYhPVgYazU5hx6emMXtargd4z