Mastering RAG Part 2: Coding with LangChain and Private Data for GenAI

Description

In this video, we dive into the practical side of Retrieval Augmented Generation (RAG) with a full coding walkthrough using LangChain and your own private data. You’ll learn how to set up a robust RAG pipeline that connects to custom data sources, embeds them into vector databases, and uses LangChain to generate accurate, context-aware responses from your own knowledge base. What you’ll get in this tutorial: • Step-by-step coding guide to implement RAG with private data sources. • Integrating LangChain to handle retrieval and generation workflows. • Using vector databases for efficient data embedding and search. • Tips to optimize and deploy your RAG system for real-world scenarios. By the end of this video, you’ll have a working RAG implementation that you can adapt to your own projects, whether for enterprise applications or personal AI assistants.