Uploaded September 2025 | Updated September 2026, 1 week ago

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code: 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:
1. Building Retreival query Pipeline With GROQ
2. Integration Vectordb in the pipeline

⚡ 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.
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Check our Complete Playlist
Agentic AI Langgraph Playlist: youtube.com/watch?v=vVGXPRjtAJE&list=PLZoTAELRMXVPFd7JdvB-rnTb_5V26NYNO
RAG Playlist: youtube.com/watch?v=fZM3oX4xEyg&list=PLZoTAELRMXVM8Pf4U67L4UuDRgV4TNX9D
MCP Playlist: youtube.com/watch?v=-UQ6OZywZ2I&list=PLZoTAELRMXVPC8r1xF68Gksi241DAtMsK