Learn How to build Advance RAG Based Project with Langchain & LlamaIndex

📚 Learn How to Build an Advanced RAG-Based Project with LangChain & LlamaIndex | Python Code Tutorial 🚀

Welcome to our step-by-step tutorial on building an advanced Retrieval-Augmented Generation (RAG) based project using LangChain and LlamaIndex! In this video, we'll dive into the world of AI-powered document retrieval and generation, showcasing how you can leverage these powerful tools to create intelligent, query-answering systems. We will learn How to improve Naive RAG with Advance RAG Techniques

🔍 What You'll Learn:

🛠️ Tools and Libraries Used:
LangChain: A powerful Framework for building LLM Based applications.
LlamaIndex: A flexible and efficient Framework for building LLM applications.

💻 Code Walkthrough:
We'll provide a detailed walkthrough of the Python code, ensuring you understand each step of the process.

📥 Download the Code:

🔔 Subscribe for More:
Don't forget to subscribe to our channel for more tutorials on GenAI, machine learning, and advanced coding projects. Hit the bell icon to stay updated with our latest videos!

📝 Links:

Join us on this exciting journey to master advanced RAG-based systems and enhance your AI projects with LangChain and LlamaIndex! 🌟

🕒 Chapters:
0:00 - Introduction
0:48 - RAG Architecture
2:50 - Advanced RAG Techniques
3:06 - Query Construction
6:14 - Python Code for Query Construction
13:58 - Text to SQL using OpenAI API
15:16 - Text to SQL with LlamaIndex Framework
17:16 - Text to Vector
24:23 - Text to Graph

#AI #RAG #LangChain #LlamaIndex #Python #MachineLearning #AdvancedAI #Tutorial #CodeWithMe #genai #artificialintelligence