Gen Ai – Llm Rag Two In One – Langchain + Llamaindex
Published 10/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Gen AI – Learn to develop RAG Applications using LangChain an LlamaIndex Frameworks using LLMs and Vector Databases
What you’ll learn
Be able to develop your own RAG Applications using either LangChain or LlamaIndex
Be able to use Vector Databases effectively within your RAG Applications
Craft Effective Prompts for your RAG Application
Create RAG Conversational Bots
Perform Tracing for your RAG Applications using LangGraph
Requirements
Python Programming Knowledge
Description
Overview
Section 1: Introduction
Lecture 1 Introduction to the Course
Lecture 2 Introduction to Large Language Models (LLMs)
Lecture 3 Introduction to Prompt Engineering
Lecture 4 Prompts Advanced
Lecture 5 Introduction to LangChain
Lecture 6 LangChain Environment Setup
Lecture 7 Installing Dependencies
Lecture 8 Using Google Gemini LLM
Lecture 9 Our First LangChain Program
Section 3: Learn LangChain through Projects
Lecture 10 Working with SQL Data – RAG Application
Lecture 11 Create a CV Upload and Search Application
Lecture 12 Create an Invoice Extract RAG Application
Lecture 13 Create a Conversational Chatbot for HR Policy Queries
Lecture 14 Analysis of Structured Data using Natural Language
Lecture 15 Introduction to LlamaIndex
Lecture 16 LlamaIndex setup
Lecture 17 Our First LlamaIndex Program
Section 5: Learn LlamaIndex through Projects
Lecture 18 RAG App using Chroma DB Vector Database
Lecture 19 LlamaIndex RAG with SQL Database
Lecture 20 LlamaIndex Query Pipelines
Lecture 21 LlamaIndex Sequential Query Pipeline
Lecture 22 LlamaIndex Complex DAG Pipeline
Lecture 23 Setting up a DataFrame Pipeline
Lecture 24 Working with Agents and Tools
Lecture 25 Create a Calculator RAG App using ReAct Agents
Lecture 26 Create a Document Agent with Dynamically built Tools
Lecture 27 Create a Code Checker RAG App
Software Developers, Data Scientists, ML Engineers, DevOps Engineers, Support Engineers, Test / QA Engineers
