https://tube.grossholtz.net/videos/watch/8359f1eb-1430-495c-a7c3-4b2c629d2638 #openai #langchain Retrieval chains allow us to connect our AI-application to external data sources to improve question answering. This is one of the most important skills to have as an AI developer, and in this video we will break down the process step-by-step. We will cover Document Loaders, Text Splitters, Embeddings, Vector Stores and Retrieval using the Retrieval Chain. ☕ Buy me a coffee: https://www.buymeacoffee.com/leonvanzyl 📑 Useful Links: Langchain Python docs: https://python.langchain.com/docs/get_started/introduction Source Code: https://github.com/leonvanzyl/langchain-python-tutorial 💬 Chat with Like-Minded Individuals on Discord: https://discord.gg/VwHZzbNawh 🧠 I can build your chatbots for you! https://www.cognaitiv.ai 🕒 TIMESTAMPS: 00:00 - Intro 01:31 - Project setup 03:26 - Retrieval Explained (RAG) 04:29 - Context 04:56 - Langchain Document 06:31 - Documents Chain 07:55 - Adding Document Loader 10:09 - Text Splitter and Chunking 13:04 - Intro to Vector Stores 14:09 - Embeddings 15:06 - Creating the vector store 16:34 - Retrieval Chain 19:21 - Testing the Retrieval Chain 19:56 - Retriever K value Mon, 06 Apr 2026 05:00:19 GMT https://validator.w3.org/feed/docs/rss2.html PeerTube - https://tube.grossholtz.net https://tube.grossholtz.net/client/assets/images/icons/icon-512x512.png https://tube.grossholtz.net/videos/watch/8359f1eb-1430-495c-a7c3-4b2c629d2638 All rights reserved, unless otherwise specified in the terms specified at https://tube.grossholtz.net/about and potential licenses granted by each content's rightholder.