LangChain Tutorial (Python) #4: Chat with Documents using Retrieval Chains
Leon van Zyl
@leonvanzylAbout
Welcome to my YouTube channel! Here, you'll find step-by-step tutorials on building real-world solutions with AI. From AI agents and automation workflows to vibe coding and agentic coding, I cover it all - using both code and no-code tools like n8n, Claude Code, Codex, Cursor, and more. Whether you're a vibe coder building your first app with AI, or a developer integrating agents into your workflow, there's something here for you. I focus on practical, hands-on tutorials that you can follow along with and implement right away. New videos every week - thanks for stopping by, and I hope you enjoy the content! Let’s work together! - For brand, sponsorship, and business inquiries: [email protected]
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Video Description
#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 🕒 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
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