2-Build RAG Pipeline From Scratch-Data Ingestion to Vector DB Pipeline-Part 1

code: https://github.com/krishnaik06/RAG-Tutorials In this video, I’ll guide you step by step to build a complete RAG (Retrieval-Augmented Generation) pipeline from scratch using LangChain. You’ll learn how to ingest raw data, preprocess it, create embeddings, and store them into a Vector Database, setting the foundation for powerful RAG applications. 🔑 What You’ll Learn: What is RAG and why it’s important Data ingestion & preprocessing for RAG Embedding generation with LangChain Storing & managing data in a Vector DB How this pipeline powers real-world RAG applications Timestamp: 00:00:00 Introduction 00:02:03 Data Ingestion Pipeline 00:08:13 Project Setup 00:11:02 Document Structure In Langchain 00:30:40 Building Embedding In RAG 00:37:22 Building Vector StoreDB 00:48:25 building RAG Retriever ⚡ Whether you’re a beginner or an experienced AI/ML practitioner, this video will give you a hands-on approach to building robust RAG pipelines. 👉 Don’t forget to like, share, and subscribe for more tutorials on Generative AI, LangChain, and RAG applications. ------------------------------------------------------------------------------ Check our Complete Playlist Agentic AI Langgraph Playlist: https://www.youtube.com/watch?v=vVGXPRjtAJE&list=PLZoTAELRMXVPFd7JdvB-rnTb_5V26NYNO RAG Playlist: https://www.youtube.com/watch?v=fZM3oX4xEyg&list=PLZoTAELRMXVM8Pf4U67L4UuDRgV4TNX9D MCP Playlist: https://www.youtube.com/watch?v=-UQ6OZywZ2I&list=PLZoTAELRMXVPC8r1xF68Gksi241DAtMsK