In this video, I show you how to use LangExtract to generate high-quality metadata for your Retrieval Augmented Generation (RAG) system. By extracting structured data from unstructured documents, we can filter results more effectively and drastically improve retrieval accuracy. I’ll walk you through a complete example, from setting up LangExtract to integrating metadata filtering into your RAG pipeline. LangExtract Video: Github: Website: RAG Beyond Basics Course: Let's Connect: 🦾 Discord: ☕ Buy me a Coffee: |🔴 Patreon: 💼Consulting: 📧 Business Contact: engineerprompt@ Become Member: 💻 Pre-configured localGPT VM: (use Code: PromptEngineering for 50% off). Signup for Newsletter, localgpt: 00:00 Metadata problem with RAG 00:45 Using Lang Extract for Metadata Extraction 01:46 Building the Retrieval Augmented Generation System 02:22 Setting Up the Environment and Sample Data 03:27 Creating the Metadata Extraction Pipeline 06:56 Implementing Metadata Filters in Vector Store 08:51 Running the Example and Viewing Results